Wed, 15 May 2024 05:44:09 -0700WeeblySun, 01 May 2022 07:00:00 GMThttp://matthewtfrazier.me/blogs/the-effect-of-machine-learning-algorithms-on-news-productionFinal Assignment for JOUR 3751 at UMN

Introduction

Before the rise of digital media in journalism, humans determined what was newsworthy using their prior knowledge and investigative research, constrained by their physical and mental capacity to write articles before a deadline, and based on their experience during their lifetime or the shared experiences and values of a newsroom. As digital media became pervasive during the 20th and 21st centuries, news has benefited from the inputs of computer algorithms that can write, research, and moderate at least as efficiently as the human journalists on which the computer scientists have based their designs.

Three aspects of news production have changed: article writing has been enhanced by machine learning algorithms, computer science has expanded the capacity of news organizations to leverage research, and finally, technology has improved the efficiency and civility of audience-provided feedback to the news organization. The importance of these technological changes has been measured economically through standard metrics like unique visitors, illustrated by a case study of Le Monde, below. Though the value of other improvements, such as greater gender inclusivity and civility, have been difficult to measure they are equally important to society. Finally, optimization of basic journalistic processes by digital technology led to more efficient newsrooms that produce more content so that they can be relevant to a wider audience.

News production before digital media

Before digital media, local beat writers would cover one sports team or one sport in their area, speaking to athletes and taking notes on a pad or recording device that required transcription, both of which required additional time to reorganize before someone could begin to write a story. News organizations were constrained by their human resources, for example, “AI tools can help journalists tell new kinds of stories that were previously too resource-impractical or technically out of reach” (Hansen et al., 2017). Physical proximity to the subjects of their stories also constrained the organizations. The financial costs of travel, in addition to the travel time that was not conducive to productive writing, have been mitigated by technology.
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Once someone had written a story during the era before digital media, it would be given to another person in the newsroom who focused on production tasks, such as layout, bylines, a headline, and images. These copy editors were experts at writing headlines based on a formula of engagement so that people would buy and subscribe to newspapers. They had a knack for selecting captivating images, too. Unfortunately, “Copy editors have been sacrificed more than any other newsroom category” (Beaujon, 2013). Algorithms are most effective when replacing human activities that are based on a “knack” because those human skills tend to use factors that can be captured and built into a computer model.

While much of the attention on artificial intelligence in the newsroom has been focused on the bias within the algorithms, it is important to understand the context of bias in newsrooms before digital media, which continues today. News production has been overwhelmingly white, and predominantly male, and remains this way today according to a Women’s Media Center analysis of the “Five Big Sunday Shows” in 2020 in which “findings confirm many years of research… as ample evidence shows, the news is fundamentally male” (Byerly, 2021). This article also refers to a study by Gaye Tuchman from over 40 years ago that quantified gender representation in news organizations as overwhelmingly white male. Perspectives offered by the news before digital media tended to follow the ideology of the writers and production staff, who were white, and though the statistics indicate that minority and female representation has not changed with the demographic changes in the United States, technology is beginning to offer insights about inclusivity and more in-depth research from more points of view.

I believe that the first step toward being inclusive is being open to feedback. We are imperfect beings who benefit from listening to others’ viewpoints. In the past, the audience of journalism wrote letters to the newspaper. Large newsrooms invested in mailroom headcount to receive and sort, then route the opinions and feedback of their readers to the parts of the organization that produced a particular section. I experienced a world that required a visit to the post office or a visit from the postman, plus a few days for a letter to travel to the news organization, then a day or more for the letter to be open, read, and sorted. The feedback loop between audience and news producer required a week or longer. Reacting to feedback about a story was a slow process in most circumstances. Also, rude or nasty letters could be sorted into a separate basket that was routed differently, to avoid offending the writer and/or the producers. The feedback loop has been one of the biggest changes in digital media.

Effect of technology in the age of digital media

Many major news organizations maintain a robot writer on staff. “Cyborg… accounts for an estimated one-third of the content published by Bloomberg News” (Contributor, 2019) and “Bertie is part of a broader focus on using artificial intelligence to make publishing more efficient for Forbes staff” (Willens, 2019). Those are just two examples of many algorithms that are writing the content for news publishers. These companies maintain a robot writer, not a staff of robot writers, to supplement the production of their human writers. “The AP estimated that it’s freed up 20 percent of reporters’ time spent covering corporate earnings and that AI is also moving the needle on accuracy” (Moses, 2017). I think the coolest news writing algorithm is GPT-3 that, or who (depending on bias toward machine learning algorithms), writes for The Guardian. Following a recent article that GPT-3 titled “A robot wrote this entire article. Are you scared yet, human?” the editor provided a brief recap of the process they used: “prompts were written by The Guardian… GPT-3 produced eight different outputs… Each was unique, interesting, and advanced a different argument.” (Editor, 2020) Like they would have done for a human writer, the editor picked “the best parts of each… Editing GPT-3’s op-end was no different to editing a human op-ed. Overall, it took less time to edit than many human op-eds.” Efficiency has been a huge benefit of algorithms, as well as several others that will be discussed later. These Robot Writers reduce the time needed to write the article, as the AP noted, and they are more accurate because computers are parameter-based technology that struggles with intuitive decision-making. They stay on topic.

The second writing technology reduces the time to write headlines and choose images, “reinforcement learning can also be applied to optimize publishing; for example, to help choose the best headlines or thumbnails for a particular story.” (Marconi, 2020). The methodology of good headlines was based on the experience of copy editors, which has been recreated in machine learning algorithms that are reactive to feedback and produce more options, more quickly (Clark, 2019). A case study of the UK press association reported, “journalists have developed templates for particular topics and use automation to create multiple variations” (Marconi, 2020) which are presented to editors as suggested headlines, images, and entire stories, just like GPT-3.

After discussing two good examples of technological efficiency, let’s examine how algorithms can affect inclusivity and gender bias. In a predominantly white male news organization, connecting stories to female and minority points of view can be difficult. Algorithms are being used by the Financial Times to track personal pronoun usage. “As reporters write their piece, the bot will alert them of any imbalance in gender ratios” and the same technology is being used to monitor images to ensure there is gender equity in coverage, “based on ‘research showing a positive correlation between stories including quotes of women and higher rates of engagement with female readers’” (Marconi, 2020). Human writers are the arbiter of this algorithmic feedback, choosing to adjust their story based on suggestions, or not, but the correlation to engagement translates to marketing revenue and higher-quality work, so it is likely that the technology will continue to be deployed.

The computer processing power and databases required to support these on-demand suggestions are immense, but the infrastructure exists already. Known as neural networks because their complexity is like “the way neurons are wired in the brain” (Hutson, 2021), these algorithms can browse massive datasets in milliseconds to find connections within the data, connections based on templates and prompts created by newsrooms. “Content and news organizations are making increasing use of AI systems to uncover data from multiple sources and automatically summarize them into articles or supporting research for those articles” (Schmelzer, 2019). A case study of the 2015 French election coverage by Le Monde explains how the news organization beat a major competitor “by having more published stories online” (Marconi, 2020) increasing page views, which is also happening in the Real Estate- and Sports-writing industries (Duncan, 2020). Quality content that is “automatically generated provides a net value for us – and our readers” has increased conversions of new paying subscribers (Kalim, 2021) just like coverage of corporate earnings.

Finally, the most obvious new technology is the Comment feature in digital media. This pervasive and ubiquitous technology enables an audience to give feedback immediately, not a week later, and anyone can access their feedback. Algorithms are being used to moderate the comments, group comments for human review, and score comments for civility. The New York Times (NYT) uses a tool called Moderator that has allowed them to open more stories for comments (Etim, 2016; Salganik & Lee, 2020). Not just an efficiency tool, the “main goal is to create a safe space for discussions” (Kovalyova, 2021) that drives engagement with readers. Internally, these moderator tools are used to fact-check reporters, too, because both processes depend on accuracy. Collaboration, a strength of networked journalism, improves the entire news-gathering process from story research and writing to the comments posted by audiences.

Personal Reflection and Conclusion

The ethical obligations of organizations that use algorithms have been altered by digital media. Bylines include references to the robot reporters based on suggested headlines and images. More news content is being written as audience participation escalates through comments. I appreciate the benefits of efficiency and inclusivity in the form of more articles that offer a broader perspective, even if the writer is a machine, or a machine influences the writer. Egalitarian systems that allow everyone to post feedback improve the user experience of journalism by eliminating the delays created by snail mail and by forcing news organizations to address alternative opinions that are posted online. 
Many of these systems exist in “black boxes” though, so only a privileged few understand the inputs and outputs of the algorithms. Transparency is being prioritized by organizations like NYT and my hope is that this open-source approach continues. I think it will continue because the business models measure the financial benefits through audience engagement. I believe that algorithms will save us in the end, because they can overcome the limits of the human mind. Only an algorithm can easily track our use of personal pronouns in our articles, and mention when we’re off track.
News writing, research, and audience participation have been changed by digital media. The slow pace of writing in the past is exponentially faster using algorithms. News producers’ dependence on “the use of algorithms to automatically generate news from structured data has shaken up the journalism industry” (Graefe, 2016). Disruption of old enterprises is a good thing, though we need to carefully monitor ethical concerns. News is being optimized by computer technology, but it is also being optimized by greater audience participation, the inclusion of more gender and minority perspectives, and giving human writers more time to reflect and revise.
 

 
References

• Beaujon, A. (2013, February 6). Copy editors 'have been sacrificed more than any other newsroom category'. Poynter. Retrieved April 29, 2022, from https://www.poynter.org/reporting-editing/2013/asne-survey-there-are-about-half-as-many-copy-editors-today-as-10-years-ago/
• Byerly, C. (2021, October 8). Why white male dominance of news media is so persistent. Women's Media Center. Retrieved April 29, 2022, from https://womensmediacenter.com/news-features/why-white-male-dominance-of-news-media-is-so-persistent
• Clark, B. (2019, December 12). [Best of 2019] Bad news, journalists: Robots are writing really good headlines now. TNW | Artificial-Intelligence. Retrieved April 29, 2022, from https://thenextweb.com/news/bad-news-journalists-robots-are-writing-really-good-headlines-now
• Contributor. (2019, May 31). Ai – how it's revolutionizing the publishing industry: What's New In Publishing: Digital Publishing News. What's New in Publishing. Retrieved April 20, 2022, from https://whatsnewinpublishing.com/ai-how-its-revolutionizing-the-publishing-industry/
• Duncan, S. (2020). NEW FORMS OF SPORTS JOURNALISM. In The Digital World of Sport: The Impact of Emerging Media on Sports News, Information and Journalism (pp. 99–112). Anthem Press. https://doi.org/10.2307/j.ctv170x59d.10   and…  (pp. 131–146). /j.ctv170x59d.12
• Etim, B. (2017, June 13). The Times sharply increases articles open for comments, using Google's technology. The New York Times. Retrieved April 29, 2022, from https://www.nytimes.com/2017/06/13/insider/have-a-comment-leave-a-comment.html
• GPT-3, Editor. (2020, September 8). A robot wrote this entire article. are you scared yet, human? | GPT-3. The Guardian. Retrieved April 14, 2022, from https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3
• Graefe, A. (2016, January 7). Guide to automated journalism. Columbia Journalism Review. Retrieved April 18, 2022, from https://www.cjr.org/tow_center_reports/guide_to_automated_journalism.php
• Hansen, M., Roca-Sales, M., Keegan, J., & King, G. (n.d.). Artificial Intelligence: Practice and Implications for Journalism. Columbia | Academic Commons. https://doi.org/https://doi.org/10.7916/D8X92PRD
• Hutson, M. (2021, March 3). Robo-writers: The rise and risks of language-generating AI. Nature News. Retrieved April 18, 2022, from https://www.nature.com/articles/d41586-021-00530-0#ref-CR1
• Kalim, F. (2021, September 22). How publishers are using robot journalism to drive engagement, subscriptions and ad revenue: What's new in publishing: Digital Publishing News. What's New in Publishing | Digital Publishing News. Retrieved April 14, 2022, from https://whatsnewinpublishing.com/how-publishers-are-using-robot-journalism-to-drive-engagement-subscriptions-and-ad-revenue/
• Kovalyova, M. (2021, February 11). Artificial Intelligence in media: Automated content opportunities and risks. The Fix. Retrieved April 29, 2022, from https://thefix.media/2021/2/11/artificial-intelligence-media
• Marconi, F. (2020). ENABLERS: THE AI TECHNOLOGIES DRIVING JOURNALISTIC CHANGE. In Newsmakers: Artificial Intelligence and the Future of Journalism (pp. 17–128). Columbia University Press. http://www.jstor.org/stable/10.7312/marc19136.6
• Moses, L. (2017, September 17). The Washington Post's robot reporter has published 850 articles in the past year. Digiday. Retrieved April 18, 2022, from https://digiday.com/media/washington-posts-robot-reporter-published-500-articles-last-year/
• Protsiuk, Z. (2020, March 17). Artificial Intelligence in the editor's seat. The Fix Media. Retrieved April 14, 2022, from https://thefix.media/2020/3/17/artificial-intelligence-journalism
• Salganik, M. J., & Lee, R. C. (2020, April 30). To apply machine learning responsibly, we use it in moderation. The New York Times. Retrieved April 29, 2022, from https://open.nytimes.com/to-apply-machine-learning-responsibly-we-use-it-in-moderation-d001f49e0644
• Schmelzer, R. (2019, August 27). AI making waves in news and journalism. Forbes. Retrieved April 21, 2022, from https://www.forbes.com/sites/cognitiveworld/2019/08/23/ai-making-waves-in-news-and-journalism/
• Willens, M. (2019, January 3). Forbes is building more AI tools for its reporters. Digiday. Retrieved April 18, 2022, from https://digiday.com/media/forbes-built-a-robot-to-pre-write-articles-for-its-contributors/ 

]]>Fri, 29 Apr 2022 07:00:00 GMThttp://matthewtfrazier.me/blogs/how-add-made-intricate-seem-straight
Cohen, Samuel, and Joan Didion. “On Keeping a Notebook.” 50 Essays: A Portable Anthology, Available from: VitalSource Bookshelf, (6th Edition). Macmillan Higher Education, 2019, pp. 118–126. ​Coping is a process that requires attention. I am grateful to Nature that short bursts of attention are all that it requires to develop coping habits. Coping connects parts of our brain that would otherwise ruminate independently, doubling effort, or more, until the mind has been pushed so far that it has nothing left to implement the repairs. When we discover the fallboard on a grand piano is stuck open, the keys exposed to dust and the ragged claws of curious cats, if it is an old piano with a simple hinge the solution is oil and elbow grease, but if it is a modern piano with a ‘slow-close’ mechanism, we must gradually lubricate the pressurized spring designed to ease the fallboard into place. Technology designed to simplify and protect needs more effort to correct. How difficult to sit in a pub and not notice the sports on the television, or worse, to only notice the television. How unsatisfying to hear your best friend yelling through the noise, unable to listen well enough to respond to much of what they say. The danger of a busy highway after a luxurious meal that lasted an hour longer than it should have because you and your friend both struggled to follow each other’s conversation, dangerous because the roadside has been cluttered with billboards that have evolved in the twentieth century from stoic, pale, immobile paintings into flashing lights that scroll high-definition images and text. Was it always unreasonable to look at one thing and expect my mind to linger on it? Realistic only for a moment, in my mind, objects connected by emotion instead of memory, which is how I have learned to cope with my boundless love of noise, unconstrained, obnoxiously in my face yet only for a moment.

ADD and ADHD offers us social, cultural, and economically adaptive advantages, though this is not a popular belief among psychiatrists. They don’t realize that medication may discriminate against those of us who have these different abilities, perhaps even undermining our potential competitive advantage over those with the favored ability to focus. Humans treat other people who are different as deficient and have done so for centuries, millennia, and likely as far back as the early hominins that lived in Africa and Asia several million years ago. We have reconstructed that in their time an extremely divided attention might help someone who needed to avoid predators. In safer, modern times, though, these advantages have become less adaptive to our disruptive environments. As our homes connect to the many buzzing, popping, blinking, beeping, vibrating, singing, and speaking devices, we lose many of the momentary meditations that occurred not so long ago when we switched from one daily activity to the next. Just a few hundred years ago, the pace to cook a meal was mediated by the speed to collect water from a pump outdoors and ingredients from the garden before returning to the kitchen to complete the task. These actions could not occur simultaneously in one room as they do today. For decades, our electronic entertainment devices required that we reach out to the machine and turn a dial to change the channel or adjust the volume. The phone cord – a line of wire connecting the speaker and receiver to the base – required that we stand nearby and limit our private offline activities to what could be accomplished in physical proximity to the phone. Those resilient daily activities constrained our focus in neatly organized stacks of tasks, end-to-end, that varied as much as a few musical notes on a page of music. Being incapable of arranging tasks, noticing only the disconnections between them, plans obliterated just by thinking about them. Never finishing anything really sucks. Knowing that the world was vulcanized by technology to be convenient, but not for me.

I have always wanted to keep a notebook, have tried so many times, but I have never been troubled by my total failure to accomplish that task because I could feel the impossibility of focus that I had learned and refined at church as a child, at so many family meals which I finished first then sat not listening to the much older adults’ conversation during, and long after they were finished too. Writing about her notebook duty, Joan Didion described it in her essay On Keeping a Notebook as, “a difficult point to admit. We are brought up in the ethic that others, any others, all others, are by definition more interesting than ourselves; taught to be diffident, just this side of self-effacing” (Didion, 1968). At home as a young boy, the motivation to succeed was primary among the adults. Admitting to my constant daily failure to pay attention, and to my failure to keep in mind what had happened to me – that day, an hour ago, a few instants – could be impossible, but I was lucky to have an ally, even one who could not understand.

Managing my attention deficit required me to learn to focus on one activity at a time. Being an English teacher, my mother decided to sharpen my attentiveness with books. Every morning, we sat on the living room couch to read for an hour or few, me with a finger tracing the progress of the story from line to line, though even that was not enough to eliminate the frustrating problem of mistakenly re-reading the start of the last line of text that had I just finished. My ADD made lines of prose dance on the page, an irregular and jagged motion like the keys on a piano keyboard forming the panoply of chords in a concerto, my mind making clusters of words swell, sway, then vacillate like the notes on sheet music. Sometimes I noticed that the word clusters in my mind were phrases that repeated: as if I had subconsciously seen the entire page in one glimpse or the entire chapter in one thought. Nature has prioritized my experience of the many above the one.

Connections were much easier for me, compared to most of the people I knew. Adapting the information at hand using a strong intuition for guessing, I could surmise answers that seemed impossible. Examples abound, but one of my favorite memories was answering my grandmother’s question that she asked her neighbor, about how far it was to a store just across the state line in New York. I thought about it for a second or less then blurted out “18 miles” which turned out to be correct. First, we would follow the main road, Kinderkamack, to the old McDonald’s restaurant – a dining institution older than fast food and literally filled to the rafters with old military equipment from the World Wars and earlier – until we reached a bend in the road near the reservoir, which would be in our way. McDonald’s was a once-in-a-lifetime memory for me – in anticipation, reminding myself of my uncle’s fable, how he was brave at my age and ordered the double cheeseburger, not knowing that a single was a double in that restaurant – and while we were there I absorbed the sight of hundreds of bombs, guns, and other “implements of destruction” as my mother would call them as an inside joke about our own little Thanksgiving tradition – listening to Alice’s Restaurant Massacre all morning before we headed to my grandparents’ house. I was about six or seven when I made my 18-mile guess to my grandmother, who bet me a GI Joe figure that I was incorrect. After the reservoir, we would drive a bit farther through a town that I believed had the most archetypically rural main street in the world – the fantasy of a boy growing up in the most densely populated suburban county in the country. I remembered how she took me there when she needed to buy fabric, or thread, something sewing-related I’m sure, even if the detail escapes me. I could picture it all in my mind in an instant, faster than simultaneously, I would learn to describe it much later in life. But, it was the famous duration of that Thanksgiving song – the inside joke with my mom, whose parents did not approve of her hippie music – that confirmed the mileage enough for me to blurt it. 18-minutes long, the song was, as I had heard on a recording by the artist Arlo Guthrie, supposing that his song was what Nixon deleted from his tapes of the Oval Office because it was the same, notorious duration. The guess was correct. It wasn’t my guess though, because it was just a prediction synthesized by the simultaneous intersection of all those thoughts.

This is not an argument against medication but an argument in favor of living with the difficult, genetic accidents that enable our superpowers and disrupt our social harmony. We might expect from daily life a gift without strings but that is not the way of Nature. Like words represent ideas inside a sentence, our bits of biology intersect to select emotional responses and physical actions with a rigorous randomness that can be appreciated only after they have happened. I worry that we are losing a requisite human skill: learning to cope. Humans have earned our adaptability by experiencing conflict then managing it. This process can be difficult, at least, and impossible at worst. Should we be grateful that adaptability has been disconnected from survival? With less competition to survive, what is the benefit? When technology usurps control of our daily activities and reduces our level of effort to conveniences and distractions, our minds are freer to wander.

If the freedom to wander leads to a more intricate sensory experience, it is also a basis for the process of coping. In their summary of a literature review about coping research, the American Psychological Association claims that “promoting patients’ use of positive coping strategies is a first-line treatment with a strong potential for helping patients develop and maintain the skills they need to live meaningful and fulfilling lives” (APA, 2020) The article’s title begins “Skills Versus Pills…” and goes on to explain the differences between Behavioral Health treatments and what is known as “usual care” with medication-based approaches. When I was first diagnosed as a young boy with ADD in the 1970s, daily doses of lithium were the approved treatment. I’m grateful to my mother that she saw the diagnosis as an opportunity for me to learn how to adapt to my predicament, yet I also acknowledge that her response was a denial of responsibility for the cigarettes and drugs that she needed while separating from her husband and filing for divorce during the first few months of pregnancy. Lithium proved to have too many horrible side effects to persist as a treatment, all of which I was spared by her decision. Instead, she freehanded my recovery using the painstaking process of close reading. 

In Milton’s epic story about Adam and Eve’s fall from grace, Paradise Lost, Satan in serpent form entices the first human female to consider his proposal that she taste the sweet, forbidden fruit on the Tree of Knowledge. As he leads her to the tree, Milton describes how the tangled serpent “made intricate seem straight, to mischief sweet. Hope elevates, joy brightens…” (Book 9, lines 632-633). I read that for the first time as a first-grader who had read almost every other book in the house and so, was forced to choose between Milton and Melville. I remember the next day, burning off energy by walking around and around the outside of my babysitter’s house singing those few words myself, reveling in the sharp T-sounds, but also marveling at the poet’s description of my life experience. By pointing at the page with my finger, I straightened the oscillating lines of the prose. By trying to cope with my easily distracted mind, I learned to filter my busy senses into meaningful intersections, high hopes, and joy.
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Learning to cope with my ADD diagnosis while I was in elementary school lifted the lid on the piano for me so that I could build my own metaphor for what was happening in my mind. The struggle to unload the obnoxious noise was a minor distraction from the actual struggle to be attentive and learn, to be curious long enough to be attentive, and thus learn how to be curious. It was like Didion’s struggle to capture her memories that she describes as “my approach to daily life ranges from the grossly negligent to the merely absent, and on those few occasions when I have tried dutifully to record a day’s events, boredom has so overcome me that the results are mysterious at best” (Didion, 1968). This daily life activity of the attentively deficient epitomizes the struggle to cope that can be a challenge, if not impossible, for so many minds. I believe the secret of coping requires some of the sweet mischief that Milton embodied in the apprehensible form of the Devil as a serpent, as observed by Eve and the omniscient narrator. It is a challenge to the prescribed condition. Intricacy was undone and straightened into lines like strings inside a piano. The strings in an open piano may be struck by any number of objects in the world, randomness constrained only by the immediate environment. But when we learn to play the instrument, like how we learn to cope, we connect random experience to measured music. 

References:

• American Psychological Association. “Skills versus Pills: Can Integrated Behavioral Health Services Benefit Depressed Patients in Primary Care?” American Psychological Association, American Psychological Association, Feb. 2020, https://www.apa.org/pubs/highlights/spotlight/issue-176. 
• Cohen, Samuel, and Joan Didion. “On Keeping a Notebook.” 50 Essays: A Portable Anthology, Available from: VitalSource Bookshelf, (6th Edition). Macmillan Higher Education, 2019, pp. 118–126. 
• Milton, John. “Paradise Lost.” The Norton Anthology of English Literature: Core Selections Ebook (Tenth Edition). W. W. Norton, 2021. [VitalSource Bookshelf].
• Robinson, P., Von Korff, M., Bush, T., Lin, E. H. B., & Ludman, E. J. (2020). The impact of primary care behavioral health services on patient behaviors: A randomized controlled trial. Families, Systems, & Health, 38(1), 6-15. http://dx.doi.org/10.1037/fsh0000474
• United Psychological Services. “Reading Problems and Add (Attention Deficit Disorder).” United Psychological Services, 19 May 2015, https://www.unitedpsychological.com/articles/real-add/reading-problems-add. 

]]>Thu, 31 Mar 2022 07:00:00 GMThttp://matthewtfrazier.me/blogs/my-five-commandments-for-algorithmsNo Black Boxes (Procedural Transparency): Refers to the transparency necessary to understand and measure the effects of outcomes and outputs. This requires proper documentation of the algorithm as well as proper retention policies to ensure the system designs, database schemas, and major product decisions have been clearly defined in detail. Ethical concerns caused by inconclusive evidence for algorithmic outputs are compounded by challenges with tracing the decision process of machine intelligence but being able to reference documentation that explains the decision tree of the algorithm ensures responsibility for agency through the attribution of action to designers, circumstances, and/or machine intelligence.
 
Predictability and Explainability: To properly attribute agency of the algorithmic outputs, the forementioned transparency is a useful tool, but additionally, the designers and technical teams that support the development of the algorithm need to ensure that their design has consistently produced the expected outputs along with the evidence to explain the logic the algorithm used to make its decisions. Most notably in unsupervised machine learning algorithms, it is difficult to differentiate between a designed outcome of an algorithm vs. an unexpected outcome based on what the algorithm learned independently. As Safiya Noble noted, algorithms are “automated decisions” that we must trust, most easily accomplished by inspection of the decision-making process to measure its predictability.
 
Aggressive Collection of Feedback (user and technical): Solicit input from the most varied group of stakeholders possible. Similar to a Works Council in Germany that includes a representative from every facet of the business – from janitorial to management to senior leadership – consideration of any new updates to the algorithm must be authorized by large, broadly representative groups of users and stakeholders. Users often have more detailed product knowledge than designers, like the poet in this week’s video, Joy Buolamwini, who noticed the problem with facial recognition technology and black skin.  Stakeholders often possess a broader awareness of extra-contextual factors that may influence the algorithm. Both groups must be frequently consulted by the teams that design, build, and maintain the algorithm.
 
Ongoing maintenance: Systematic collection of feedback from users will identify problems with the current design and opportunities for improvement based on new technology and the wisdom gained through experience. Sometimes, an algorithm will do harm to its users, so it is essential to continuously solicit feedback, analyze reporting produced by the algorithm, and aggressively monitor performance trends. Observations or reports of harm to users should generate an immediate maintenance response from the design team and a fair evaluation of the potential need to suspend the algorithm based on the scale, and correctability, of the harm. Since most algorithms are more complex than a checkers game (which we learned has been process-mapped to prevent algorithms from losing a match) the proper balance of accuracy and efficiency, a core value of algorithms, will almost always require more updates in the future.
 
Data Security: Require a minimum standard for the protection of data created by the algorithm, stored by technical teams, and used by additional parties. To accomplish this goal, using the best hardware, firmware, and software will be expected, but most importantly, using the right technology to minimize the risk of a data breach, and any potential misuse of the data. Privacy is a right in some countries, protecting individual autonomy by limiting the unauthorized collection of data, as well as improper use. Most algorithms create more data than a human mind can monitor, so implementing the right tools – building them if necessary for special requirements – will protect data throughout the end-to-end process. Processes should be designed to protect data and all relevant work teams should be fully trained in data security.]]>Thu, 24 Feb 2022 08:00:00 GMThttp://matthewtfrazier.me/blogs/ethics-of-machine-learning-algorithmsMidterm Assignment for CI 4311W at UMN 

Introduction

One of the greatest accomplishments of Artificial Intelligence has been Machine Learning Algorithms (MLAs). They are not the same as average, everyday algorithms with a ‘little a’. The biggest difference is that MLAs are semi-autonomous at least, sometimes entirely autonomous, and if either condition is true, that creates ethical complexities related to individual responsibility, attribution of virtues, vices, and beneficiaries, as well as the transparency necessary to understand and measure the effects of outcomes and outputs. Furthermore, the autonomy of MLAs puts them into a unique class with ambiguous rights and duties. To unravel some of these complexities, at least enough to engage in a basic ethical discussion about them, we need to examine a machine-oriented definition of intelligence and rationality. We will need to reflect on problems as old as the Wizard of Oz, the movie through which the world witnessed the transformation of moving pictures from black-and-white to color; a radical change. The transformation of algorithms to MLAs has a comparable scale, i.e., from two colors to infinite colors.

A simple and well-known example of an MLA is the spam filter in email; semi-autonomous because we click buttons in unsolicited emails to help train it. On the other hand, the “k-means clustering” MLA learns and operates unsupervised, independently discovering answers to questions about observations that have not occurred in a human mind yet.  These MLAs exceed the brain’s capacity to process information.  I will use this type of algorithm as a reference point because I have worked directly with it to build global fraud prevention programs. On several occasions, my brain was the first step of the validation process to ensure that a newly coded k-means clustering analytic tool was accurate and useful. I am neither mathematically inclined, nor scientific – my input was the external reference point. It was important that I did not fully understand the mechanisms of MLAs, and that remains true today.

Ethics of Evidence

My human brain was the bulwark to filter inconclusive evidence, an epistemic ethical concern. Algorithms “encourage the practice of apophenia: ‘seeing patterns where none actually exist, simply because massive quantities of data can offer connections that radiate in all directions’ (Boyd and Crawford 2012, 668)” (Tsarnados et al., 2021). In ‘The ethics of algorithms: key problems and solutions’ a team of researchers provides two additional types of evidentiary concerns, both more ethically concerning: inconclusive evidence and misguided evidence. The latter creates risks to justice and fairness because neither the algorithm’s designers nor the MLA has been well prepared to assess the social context that the outputs affect. In addition, once the MLA has been implemented, the technical team will move to a new project or new jobs, creating a risk of context transfer bias should the new owners of the algorithm apply it to a novel problem. Ethical concerns with inconclusive evidence are compounded by challenges with tracing the decision process of machine intelligence.

This transparency problem is another significant ethical concern related to the traceability of machines’ learning and subsequent decision-making. The ability to understand algorithms is limited to a relatively small audience, often affluent and educated, furthering social inequality as MLAs become more prolific and integrated into our basic tools for daily living. Documentation is too much for users, too difficult to produce by business, and slows innovation in return for ubiquitous benefits. A related problem is attributing responsibility for the actions of MLAs. “The technical complexity and dynamism of ML algorithms make them prone to concerns of ‘agency laundering’: a moral wrong which consists in distancing oneself from morally suspect actions, regardless of whether those actions were intended or not, by blaming the algorithm (Rubel et al. 2019)” (Tsarnados et al., 2021). The compound effect of a small group of knowledgeable owners of machine learning algorithms and the general difficulty in determining when an algorithmic output is the result of human design or independent and unsupervised machine learning leads to conflicts of moral duties. These conflicts are created by the black box in which the algorithm exists and the utilitarian difficulties created by challenges predicting consequences when only a small group of experts control the machine, or the machine makes decisions by itself based on the rules it has written, which are based on the learning in which it has engaged independently. It is important to note that transparency is a human problem too because most people cannot explain the ’what, how, and why’ for every action. Presuming that this human limitation is accurate, we would be treating MLAs unfairly by expecting it in their domain.

Intelligence, Rationality, and the Ethics of Agency

Two common philosophical definitions of Intelligence apply to our understanding of MLAs. First, intelligence is the capacity to achieve complex goals, and second, intelligence does the right thing at the right time. “The teleological framework of multiple goals, within which humans move about balancing reason and emotion, brings us back to Aristotle’s view of ethics as the pursuit of the good life through a well-orchestrated ethos. Aristotle highlights the capacity to listen as one of the key competencies of ethical-practical reasoning, phronesis, which he also calls a ‘sense’” (Holst, 2021). In my work validating MLAs’ k-means clustering analysis, it was what had been professionally labeled my “Spidey sense” for uncovering complex, well-hidden webs of fraud that was being compared to the outputs of the intelligent, unsupervised machine. It is important to note that my intuition was less transparent than the outputs of the machine that was being built to replace me. Predictability and explainability are both important aspects of virtuous decisions. ‘An FDA for Algorithms’ highlights “the analogy between complex algorithms and complex drugs. With respect to the operation of many drugs, the precise mechanisms by which they produce their benefits and harms are not well understood. The same will soon be true of the most important (and potentially dangerous) future algorithms” (Tutt, 2016).

The prospect of becoming a full ethical agent is a human problem that applies to MLAs. For Plato, it is a two-part problem, first, “in his dialogues, the crucial capacity for rational thinking is called ‘logon didonai’ which means to give an account of something” (Holst, 2021). We have explored that problem of transparency already. The second part, developed further and adopted by Aristotle, includes desire and emotion – both mastery and the use of those irrational components of reason. How does one apply this second part of being a full ethical agent to a machine? This is the existential dilemma faced by the Tin Man in the Wizard of Oz: he does not have a heart and thus, cannot experience emotion. One can imagine a machine designed for this purpose, but the complexity of designing a problem-solving machine like an MLA with these components embedded seems unfeasible and impractical. “In contrast to reinforcement learning methods, both supervised and unsupervised learning methods are… poorly suited methods for the raising of ethical machines” (Kaas, 2021)

Two topics mentioned in different articles in the book “Machine Law, Ethics, and Morality in the Age of Artificial Intelligence” can be combined to propose a solution. In a game called Cake or Death, the subject will be presented with two options, both of which lead to ethically appropriate rewards – bake a cake or kill three people. There is a third option, too, after which the program loops back to the original decision – ask a virtual entity for advice. Additional, more complex, games were outlined in Kaas’ article, but at the core of each one was a nudge of the machine to learn to ask for help when making decisions, and they present all decisions as benefitting from this ethical process through a reward mechanism. With this approach, ethical MLAs would be designed, implemented, and maintained for use by other MLAs that need this specific kind of advice, using common technological procedures like today’s APIs that already request and receive the high volumes of data needed to support frequent ethics checks.

Conclusions and Implications

These are just a few of the ethical concerns with machine learning algorithms. One simple solution addresses concerns with the ethical categories that are the broad focus of this paper (Rights, Utilitarian Good, Fairness, Common Good, and Virtue): ongoing maintenance. The technical teams that build these machines will transition to new roles and projects. Contexts in which these machines are used will change over time. The mathematics and science that created MLAs will produce new discoveries that will incrementally, sometimes fundamentally, change the technology. For these reasons, maintenance is an ethical requirement for the builders and owners of MLAs, even when these roles are taken over by the MLAs. Maintenance enables input from the participants, users, and social contracts. The environment in which technology develops is naturally iterative so the maintenance will not be a burden to development. Also, using the ethical guidelines of pharmaceuticals may inform the ethics of intelligent machine learning algorithms. AlphaGo and Deep Blue were successful in their domains because they used probability theory refined through deep self-reflection achieved through the machines’ observations of the games they played against themselves. A machine playing a game against itself appears to be an independent entity, which is why they call that machine learning process unsupervised. But the games these two MLAs play have minimal ethical concerns compared to the medical advice, fraud prevention, and language processing that the more well-integrated MLAs will produce. 
Finally, after his thorough discussion of the Black Box and Tin Man problems with artificial intelligence, he ends optimistically, and I would like to echo his thoughts as I conclude. “Due to a mismatch of our rational and emotional nature, many of our human ways of acting and thinking are flawed with incoherent, contradictory, and biased elements that still stand in the way of realizing fully ethical rationality. Should the future development of AI become truly ethical, it will also help us become more ethical.” (Holst, 2021) Because it is most likely that one day, we will rely on the outputs of MLAs in most aspects of our lives, assessing the ethics of our design decisions is an essential component of the MLA design process today. Successful, independent learning machines may help us overcome some of the human challenges that we all experience every day, most notably the struggle between emotional desire and our rational intelligence. It is my belief that these two constitutional aspects of humanity are not only the result of natural processes outside of our control but also the result of the learning processes that we engage throughout our lives. Because our machines have achieved the threshold of unsupervised learning, like their original makers, these infinite decisions from which we learn can be traced back to the black-and-white of ethically good or bad.

 
References

• Holst, J. (2021). Ethical Rationality in AI: On the Prospect of Becoming a Full Ethical Agent. In S. Thompson (Eds.), Machine Law, Ethics, and Morality in the Age of Artificial Intelligence (pp. 69-84). IGI Global. https://doi-org.ezp1.lib.umn.edu/10.4018/978-1-7998-4894-3.ch005
• Kaas, M. H. (2021). Raising Ethical Machines: Bottom-Up Methods to Implementing Machine Ethics. In S. Thompson (Eds.), Machine Law, Ethics, and Morality in the Age of Artificial Intelligence (pp. 47-68). IGI Global. https://doi-org.ezp1.lib.umn.edu/10.4018/978-1-7998-4894-3.ch004
• Morse, L., Teodorescu, M., Awwad, Y., & Kane, G. (2021). Do the Ends Justify the Means? Variation in the Distributive and Procedural Fairness of Machine Learning Algorithms. Journal of Business Ethics, Journal of business ethics, 2021.
• Thompson, S. J. (Ed.). (2021). Machine Law, Ethics, and Morality in the Age of Artificial Intelligence. IGI Global. https://doi-org.ezp1.lib.umn.edu/10.4018/978-1-7998-4894-3
• Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2021). The ethics of algorithms: Key problems and solutions. AI & Society, 37(1), 215-230.
• Tutt, Andrew, An FDA for Algorithms (March 15, 2016). 69 Admin. L. Rev. 83 (2017), Available at SSRN: https://ssrn.com/abstract=2747994 or http://dx.doi.org/10.2139/ssrn.2747994

 ​]]>Fri, 10 Dec 2021 08:00:00 GMThttp://matthewtfrazier.me/blogs/creativity-and-intelligence-in-applied-educational-psychology-final-reflectionTWhile reviewing my prior reflections to help me prepare for this final paper, I noticed that Vygotsky was at the top of my mind when we started to explore Creativity during module one. He returned during the second-to-last lesson about cognitive mechanisms of Intelligence in which we focused on scaffolding as well as the zone of proximal development. His ideas seem the most natural and appropriate to well-developed learning environments that improve students’ self-efficacy by keeping both parties of the educational experience – student and teacher – investing in appropriate levels of engagement. This fits very well with Wallas’s model of creativity as well as Palincsar’s elements of cognitive instruction in which intentional learning requires the teacher to view all students as capable of the classwork, and most importantly, communicate consistently as they work together. This class has enabled me to expand prior knowledge from a summer class about Learning, Cognition and Assessment with additional connections that clarify for me the learning relationship between creativity and intelligence. As the lecture notes stated, “Helping students learn intentionally… will empower them to become independent learners.”

I was pleasantly shocked while preparing my extra credit assignment when I encountered several achievement tests used to measure the impact of chess training on math, reading, and meta-cognitive skills. First, I was shocked because I had stopped feeling dismissive of the tests as ‘just another IQ estimate’ because of the time we used to deep dive into the intelligence tests at the midpoint of this semester. Second, I was shocked because as I looked up the tests online, they reminded me of the Digit Span module in the WCIS test as well as the Sequential Processing and Achievement subtests in the K-ABC test, and the section in Binet’s test for Quantitative Reasoning came to mind as I read more about the SPM for math assessment. It felt good to be comfortable with the content in these tests and it felt good to read them and more quickly understand what they meant, so I spent less time on that deep dive. The case studies, the scholarly article review, and the extra credit assignment provided so many opportunities to learn about the scientific study of psychology and that changed my perception of intelligence testing in general. Additionally, the exposure to the reports in the case studies helped me develop a format to organize the information in the scholarly article and the extra credit projects, prioritizing incisive brevity in the summary, being clear upfront about the mechanisms used by the study and their weaknesses, then leading with the successful results because they were most interesting to my audience.

A significant difference between creativity and intelligence that became more apparent to me while working on the material for this class will be useful as I continue my education here at UMN. Externalization is a foundational aspect of creativity – both little-c and big-C – because an idea requires more than the mind of the beholder to be creative. On the other hand, the evidence about Intelligence implies such a biogenetic causation that sharing probably is not required. Sharing is a huge part of creativity – in individualistic and collectivist societies alike despite each culture’s drastically different response to it. Intelligence is a state of being that may be recognized and measured with a test, by successfully completing a task at work or in school, or by a count of patents. That last measure is where it overlaps with Creativity but the difference is significant. While most of the course material about Intelligence can be categorized as one or both of two of Cattell’s five factors, fluid and crystallized intelligence, the multi-factorial generalized intelligence is the mechanism that overlaps with creativity and can be seen in measurements such as patents. At Amazon, where I worked for so long, patents have been an important aspect of career development at the highest levels. Many of the senior executives have at least one and some of the top leaders have twenty or more. Working as we did during the beginning of retail e-commerce era, patent ideas were a bit like fish in the barrel, but those executives have the extreme fluid intelligence (internally, named “intuition”), and a broad scope of crystallized knowledge, and they use both creatively to transfer ideas between domains and show off their consistently good judgement through high velocity decision-making every day.

Finally, I am thankful for Ritchie’s debunking of “learning styles” in the opening pages of his book because it provided good reasons for the skepticism I have always felt about the concept. Later, as we learned about more modern approaches to intelligence, the differences in intelligence that were caricatured by “learning styles” developed into the modern, more reasonable approach of multi-factor intelligence, for example Sternberg’s triarchic intelligence which I most certainly prefer. My goal is to work in the public high school system where I will encounter many different types of students and parents with different educational backgrounds. After this class, I feel more prepared to apply what I have learned in this course about scaffolding, fluid and crystallized intelligence, and the importance of externalizing creativity to my classroom, lesson plans, and assessment strategies. 
]]>Tue, 07 Dec 2021 08:00:00 GMThttp://matthewtfrazier.me/blogs/final-policy-speech-technological-unemploymentTitle: The U.S. Congress Should Expand the TAA Program to Include Technology

Specific Purpose: Explain the technological unemployment exigency and show that income replacement through the TAA Program is the best solution.

​Central Idea: Algorithms are destroying American jobs for the first time in history and the US Congress should expand an existing, successful income replacement program to help workers.


INTRODUCTION

1. Attention-getter: You might think we cannot automate creativity, but you would be wrong.  Algorithms have been beating the world’s chess and Go champions for years and they are proving to be better than American service workers, too.
2. Demonstrate audience significance: Kelly (2020) Forbes: You probably know one the 60 million people that the World Economic Forum counted as unemployed because an algorithm does their job more efficiently at almost no cost to the business, and you can pass legislation to protect American workers like the US Congress did in the 1960s when facing the same problem but from International Trade.
3. Enhance your ethos: In just over a decade of work at Amazon.com, my algorithms have automated the jobs of tens of thousands of human workers and each time we implemented the change successfully, we earned tax breaks from the government that we invested into new automation for the remaining jobs on our customer service and finance teams.
4. State the central idea: Algorithms are destroying American jobs for the first time in history and the US Congress should expand an existing, successful income replacement program to help workers.
5. Preview: I will briefly recap the problem caused by technological unemployment before discussing the best solution and a counter argument, and finish by talking about the security and well-being of the nation.

The loss of jobs to technology has a very long history that until recently was good for human workers.

What is Workplace Automation?

1. In 2021, many people don’t realize that Automation has been happening for thousands of years.
   1. Aristotle (2012) Duke Classics, in Politics, written in the 4th century BC, “for if every instrument at command or from a preconception of its master’s will could accomplish its work, obeying or anticipating the will of others… the shuttle would then weave and the lyre play of itself.”
   2. Tech can replicate valuable ideas and processes at very low cost.
   3. This creates convenience for society and wealth for owners of the tech, but it also diminishes the demand for many kinds of labor.
2. The historical fear of jobs loss to tech is called the “Luddite Fallacy.”
   1. In the 19th century Luddites tried to protect their livelihoods by smashing looms, typesetters, and other industrial machines.
   2. They thought that destroying the machines would destroy the threat of tech replacing them, but they thought wrong.
   3. Tech unemployment has been happening for three-thousand years, but it is only recently that human workers lose more jobs than automation creates.
3. Technology advances have tended to favor improved working conditions for humans, productivity gains, and net increases in new jobs, until the last 34 years.
   1. The danger technology presents to the social well-being of society has been growing for over a century.
   2. But according to Dizikes (2020) MIT News: Prior to 1987, jobs lost to automation were replaced by 2% more jobs, but after 1987 jobs lost to automation destroyed 6% of new opportunities.

Understanding why this change has occurred is very important; the game of Go and the Uber Effect are two good examples that will help us understand ‘the why’…

B. Algorithms are more than a replacement for a process because they are becoming more creative than the programmers who make them, and more efficient than the human workers they were designed to enhance.

1. My background in creative writing and poetry gave me a significant advantage when competing with scientists and programmers for jobs to work on projects that required us to build automation.
   1. For me, the writing process is about realizing when a mistake was a creative, positive way of saying something, or seeing the world.
   2. I applied that same approach to building automation.
   3. When many people thought a computer program had failed, I found ways to use what we learned from our mistakes to make the next version even better, even when that was not what we originally intended.
2. Algorithms have been beating the world’s chess and Go champions for years because they are more efficient tacticians, but even more so because they can discover and make extremely creative moves.
   1. About 5 years ago, AlphaGo was the first algorithm to beat a Go grandmaster and it’s move #37 in the 1st game has been widely discussed in machine learning research and blogs.
   2. International observers of the matches noted that AlphaGo’s moves were so original that they intimidated his human opponent; maybe most intimidating when it seemed to be making a mistake.
   3. We’ll never know if move 37 was a software error, a mistake in an otherwise perfect game, or the remarkable insight of a machine unrestricted by human convention, but it won the game.
3. The Uber Effect is a clear example of how an algorithm intended to be a more convenient taxi experience destroyed a more than century-old industry, the NYC cabbies. Goldstein (2018) Forbes:
   1. In 2014 the market was 8% rideshare, 55% rental and 37% taxi and NYC Taxi medallions were worth $1.2M.
   2. In 2018, the Uber effect rocketed rideshare from 8% to 70%, rentals were cut in half but still retained 23%, but most importantly - cabbies were left with only 6% of total rides, an 80% decline.
   3. Even worse, in those 4 years, the value of taxi medallions dropped 90% to just $120,000; I’ll return to this topic later when I discuss the practicality of the solution.

Automation of hailing a ride by Uber and Lyft eliminated the business for 9 out of 10 New York City cabbies proving that Algorithms can be as dangerous as manufacturing jobs moving overseas, yet this is a problem that can be solved much like it has been solved before.

C. The Proposal

1. I am here to advocate for a lost wage replacement insurance program for American Workers.
   1. With great foresight for the coming globalization of business, the US Congress in the 1960s created the TAA program (an acronym for Trade Adjustment Assistance) to protect American Workers who would lose their jobs to lower cost labor markets overseas.
      1. Key terms of the program include full income replacement for two years via direct payments, relocation allowances so workers can move to employment opportunities, and case management services such as career counseling and referrals to support services for food security or childcare.
   2. Stettner (2018) Century Foundation detailed how to expand the TAA program to include jobs lost to automation.
      1. It is easier to add one new eligibility requirement to an already successful unemployment insurance program than it is to build and fund an entirely new program.
   3. Stettner (2018) Century Foundation: “Expand TAA to Include Technology: The Extra T in TAA… This would be a single program in terms of benefits provided but with added qualification rules for technology on top of the current rules for trade… to respond to the challenges of job loss due to technology.”
2. Some who oppose targeted support for unemployment caused by tech cite the TAA programs flaws but it is important to note that the program has been reauthorized three times by both parties in the last 70 years, as recently as 2017 along with a few changes to expand benefits.
   1. The program enabled legislators to grow international trade.
3. Despite evidence that automation destroys 6% of jobs when it once created new opportunities, some ideological groups deny the problem and won’t accept any cost in response to this cause of unemployment.
   1. Cass (2018) National Review: Orren Cass wrote an article in 2016 for the National Review that was republished on many different websites about why he thinks income replacement is a bad idea, stating that it “would only entrench our misconceptions about the relationship between the individual and the state.”
      1. But America already established a relationship between the individual and he States in the form of existing unemployment programs.
   2. He thinks that worker protection programs eliminate an “essential role as the way to earn a living, work would instead be an activity one engaged in by choice, for enjoyment, or to afford nicer things.”
      1. We need to protect all workers from hardships created by jobs lost to tech, and work is so much more than a means to afford nicer things.

The cost of doing nothing is huge and will cause suffering that leads to hasty action by local & national governments.

D. We should understand the impact of expanding the TAA to include Tech jobs.

1. Returning to the Uber example, the New York City cabbies just ended their hunger strike a few weeks ago and the cost of the government support program will be $140,000 per cabbie!
   1. Just 5K cabbies in NYC will cost the local and federal government $750M to account for wages lost to algorithms.
   2. Imagine the cost to support the more than 60M workers who are projected to lose their jobs to automation in the next ten years.
      1. Using the cabbie bailout cost, a bailout for 60M would cost $12 trillion.
      2. We can’t fund that much, but the Trade Adjustment Assistance program because it has proven to be successful.
2. It is easier to add one new eligibility requirement to an already successful unemployment insurance program than it is to build and fund an entirely new program from scratch.
   1. The TAA program proved that it is more effective to deploy capital directly to the worker, instead of through a complex job training or placement program that is managed at a federal level.
   2. Federal legislators struggle to understand what will work best at local levels and should rely on individuals to determine the best path forward and States manage unemployment system already.
3. Reflects our ideals of the power of the American individual as a targeted response to unemployment caused by automation.
   1. Individualism – Americans can figure out how to best use the income replacement dollars.
   2. TAA provides relocation services and direct case management for each employed worker so they can make the best decision based on their family’s circumstances.
      1. Atkinson (2018) ITIF: “Rather than slow down technological disruption… policymakers should focus on doing significantly more to help those who are displaced transition successfully into new jobs and occupations.”
      2. Same as Trade job loss; job training programs are not as effective due to the location of new jobs and the existing skills of the workers who lost their jobs to technology.

In conclusion, let's review and reiterate the main ideas and suggestions we reviewed today during this presentation.

CONCLUSION

1. Review of main points: Technology has encroached on our jobs for millennia, but only recently has it taken more than it gave back. Millions of workers have been affected and millions more will be affected and lump sum, knee-jerk reactions will cost huge pots of money. 70 years ago, the same problem existed for international trade, but the Trade Adjustment Assistance program has allowed the US to invest in Trade knowing that workers would be protected. We should add one requirement for Technology to it so that the program protects workers from jobs lost to automation, too. The problem is equally real.
2.  Reinforcement of Central Idea: Algorithms are destroying American jobs for the first time in history and the US Congress should expand an existing, successful income replacement program to help workers.
3. Final idea: The foresight of the US Congress in the 1960s prepared the United States to manage unemployment caused by trade policies and the cost of labor overseas. We should have that same foresight today and take action to rename the TAA program as the TTAA program because technology is now just as dangerous to American jobs as international trade.

References

• Aristotle - translated by William Ellis (2012) The Politics of Aristotle. A Treatise on Government.  North Carolina: Duke Classics.
• Atkinson, R. D. (2018, February 20). How to reform worker-training and adjustment policies for an era of technological change. How to Reform Worker-Training and Adjustment Policies for an Era of Technological Change. Retrieved November 16, 2021, from https://itif.org/publications/2018/02/20/technological-innovation-employment-and-workforce-adjustment-policies.
• Cass, O. (2017, March 21). Why a universal basic income is a terrible idea. Manhattan Institute. Retrieved November 15, 2021, from https://www.manhattan-institute.org/html/why-universal-basic-income-terrible-idea-8984.html.
• Dizikes, P. (2020, May 5). Study finds stronger links between automation and inequality. MIT News | Massachusetts Institute of Technology. Retrieved November 16, 2021, from https://news.mit.edu/2020/study-inks-automation-inequality-0506.
• Goldstein, M. (2021, June 30). Dislocation and its discontents: Ride-sharing's impact on the taxi industry. Forbes. Retrieved November 16, 2021, from https://www.forbes.com/sites/michaelgoldstein/2018/06/08/uber-lyft-taxi-drivers/.
• Kelly, J. (2020, October 27). U.S. lost over 60 million jobs-now robots, Tech and artificial intelligence will take millions more. Forbes. Retrieved November 16, 2021, from https://www.forbes.com/sites/jackkelly/2020/10/27/us-lost-over-60-million-jobs-now-robots-tech-and-artificial-intelligence-will-take-millions-more/?sh=e02a4501a525.
• Stettner, A. (2018, April 26). Mounting a response to technological unemployment. The Century Foundation. Retrieved November 16, 2021, from https://tcf.org/content/report/mounting-response-technological-unemployment/. 

]]>Thu, 02 Dec 2021 08:00:00 GMThttp://matthewtfrazier.me/blogs/creativity-and-intelligence-in-applied-educational-psychology-post-7Module 7 – Teaching and the components of intelligence

1. Perkins Survey (1995): Feuerstein’s IE; deBono’s CoRT; Lipman’s Philos.4kids & Project Intel’
2. Palincsar; and Stone and Reid – improving thinking skills for LD students
3. Expertise – Perkins, Mackintosh; Domain-specific's and General Intelligence's role in expertise
4. Training to develop Cognitive Complexity
5. Brief Survey of findings regarding intelligence research

This was my favorite module for several reasons. First, I am starting to feel acquainted with the major theories of intelligence and more experienced with how they overlap and how they contrast. I read the Ritchie book on vacation last August and was totally unfamiliar with the concepts and references, but it was not until this Module’s reading that encountering the names of education enrichment programs and Luria in Chapter 5, as well the Bell Curve in Chapter 6 had meaning greater than the scope of the passage. Two weeks ago in a discussion that was focused on a single intelligence model in Anthropology class, I compared Sternberg’s triarchic approach to intelligence and the factor analytic approaches. Even though that extra context digressed the discussion further than needed for an evolutionary evaluation of differences between the Homo species and Hominins, it felt good to be comfortable with the material.

​The introduction to Instrumental Enrichment (IE) will be useful when creating lesson and assessment plans as an English teacher. Vygotsky’s approach is already my preference, but the four features of IE are excellent complements for teaching programs designed to promote self-efficacy and positive feedback loops with my students. The zone of proximal development is expanded by IE’s more challenge-focused requirements, to strengthen areas of weakness through information input, elaboration, and engaging assumptions in output through positive self-reflection, as well as through finding a starting point that requires the least prior knowledge. I imagine that scaffolding could be perceived as coddling, or not providing sufficient challenge to students, and these features of IE provide an effective counterpoint. This approach was deepened by the information presented about Cognitive Instruction and Intentional Learning, notably prolepsis and reflective abstraction. One of the test questions for this module reinforced the importance of purposeful goals that are transparent to the student. This is also an important feature of managing at scale, for example, when my direct reports were hundreds or even many thousands of miles away in another country. Actionability of a goal can be directly observed as it is created when all parties engage in a reasonable discussion of the intent of the goal.

When this module turned to a review of expertise, it provided a chance for me to return to ideas I had considered during the study of creativity, notably how eminence was most often the most objective factor used to identify judges of levels of creativity. In this module, expertise is the synthesis of reflective and experiential intelligence much like fluid and crystallized intelligence creates a measure of general intelligence. As a manager of departments about which I knew only the basics and for which I had had no formal training, I can attest that domain-specific knowledge is a strong predictor of professional competence, something sought when investing long hours trying to find the right candidate for a job. Fluid intelligence and the ability of someone to reflect on their domain knowledge so that it can be more easily applied to new scenarios is also a major factor because real-world problems always have plenty of ambiguity.

Finally, I thoroughly enjoyed the statistics and factoids presented in the summary at the end of the lecture notes. Except for the seventh point, they were meaningful reminders and additional context about prior lessons. The correlation data was most meaningful to me because it provides sound basis for the belief that intelligence is most strongly influenced by genetic factors. The data in the sixth point succinctly states the case in favor of genetics.]]>Fri, 19 Nov 2021 08:00:00 GMThttp://matthewtfrazier.me/blogs/creativity-and-intelligence-in-applied-educational-psychology-post-6Module 6 – Contemporary Views of Intelligence

1. Herrnstein & Murray – Bell Curve
2. Modular Perspectives: Ceci; Kargopoulos and Demetriou
3. Triarchic – Sternberg; Tripartite – Perkins; Multiple Intel – Gardner 

Though I was generally aware of the Bell Curve due to the media coverage portraying it as controversial, after learning that low IQ correlates to crime, risk for being on welfare, and even likelihood of worker disability, I understand why the book seemed threatening to some groups. So often, narratives based on academic study become the basis for propaganda with the wrong intentions. As a manager I have advocated for budgets that included near-constant increases in well-being costs based on neuroscientific data about the benefits of enabling work-life balance, providing physical space to relieve stress, and the virtuous cycle created by consistent “waffle Fridays” and “Tuesday Night Happy Hours” for my organization. Herrnstein’s and Murray’s evidence clearly advocates for some of their propositions, such as supporting top performers, but I did not understand how school choice was beneficial unless they intended it only for the gifted. All of that said, my first finding from this module – which absolutely shocked me – is the data that in 1993, 92.2% of federal education budget was directed toward the disadvantaged and less than one-tenth of one percent was allocated to gifted students. While it is difficult to compared based on the 2020 budget information available online, of the $64B, $30.7B of just discretionary funding has been clearly allocated for disadvantaged groups, to “support high-need students through essential formula grant programs.” (US Dept of Education, 2020) 

A second significant finding based on my plan to teach was the triarchic model of Sternberg. Intuitively, my understanding of intelligence is based on Analytic, Creative, and Practical components – closely aligned to his model. Having read his scholarly paper about the application of his triarchic model in the classroom, my understanding of Successful Intelligence seems complimentary to the Vygotsky model of scaffolding that enables the student with an emphasis on self-efficacy and appropriate support. While I agree that the cladistic view of the triarchic model presented in the lecture notes does not differentiate inductive and deductive reasoning, I strongly favor his triarchic model of intelligence. Further, based on the data presented in his paper that showed achievement benefit in both performance assessments and objective assessments, the theory will be useful when identifying how to structure a classroom activity or a test so that all of the students will have an opportunity to learn, and to apply their knowledge based on the component ability in which they are strongest.

Finally, the positive correlation between political engagement and intelligence was another finding. It gives me hope for a progressive future for this world to see that r = 0.45 for IQ and social liberalism, which is much higher than I would have guessed. For example, Ritchie’s data that shows higher-IQ correlates to increased interested in politics in general, being more likely to vote in elections. Furthermore, Ritchie’s information relating the physical aspects of the brain to intelligence corresponds well with the Anthropology class I’m taking as we focus our study to the Homo genus after several weeks of studying the endocranial capacities of species that are the bridge between apes and humans.]]>Thu, 18 Nov 2021 08:00:00 GMThttp://matthewtfrazier.me/blogs/analysis-of-a-hegemonic-ideologist-view-of-universal-basic-incomeFollowing a century during which the proverbial ‘hand of government’ replaced the fickle assistance meted by monarchs and oligarchic elites, US policy makers and advocates now have a robust dataset on which to perform impact analysis of social welfare benefits, income insurance policies, and the effect of poverty protection programs on work incentives. The white paper, “Why a Universal Basic Income Is a Terrible Idea” written by Oren Cass and originally published by National Review on June 15, 2016, presents a conservative response to all Universal Basic Income (UBI) proposals which are a predominantly liberal solutions to two significant exigencies in 21st century America – poverty and technological unemployment. Cass’s white paper implies a close relationship exists between both problems, but the following analysis will be focused on the latter issue as it is the topic about which I am writing policy response speeches.
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The cost of any UBI policy will be significant, diminishing the wealth of a nation’s highest earners to redirect income to meet the basic needs of people who earn the least. Supporting arguments as well as those against such a policy attempt to influence that wealthy group who controls the money today, and historically, most often controls the levers of power necessary to implement any such policy change. Whether or not the possession of money is directly related to the control of power, and the moral or ethical implications of that relationship, has been set aside by this discourse because the focus is always on one side of the exigency, or the other.

Supportive arguments that favor UBI provide historical analysis, data-driven rationales, and anecdotes that attempt to engage the audience through enthymeme. Oppositional arguments, for example Cass’s white paper, tend to leverage coercive language based on institutionalized fears of change, discussions of social class, and generalized examples of potential financial challenge. In this paper, I argue that Cass’s argument is not persuasive because he constitutes an audience distrustful of facts, he ignores basic math when estimating costs, and inaccurately labels everyone who is not part of his ideology as outsiders and less worthy. Ultimately, his terrible argument against UBI disconnects from reality, disengages from the exigency it claimed as its topic, and dissolves into false claims and racist tropes that demean American citizens.

Literature Review

Audience as the foundation of argument enables the rhetor to choose the right word, form the most influential argument, and present evidence based on what is required according to the expectations of the immediate reader as well as the many, virtual readers who the author cannot necessarily presuppose. “A reenergized citizenry committed to carrying on the fight” (Campbell et al., 2015, p.30) aptly describes the audience for Cass’s white paper as a group of citizens, historically connected to the society – in this case Americans – who have been enthused to action by progressive arguments that threaten their grip on power and wealth. They engage in the good fight because they believe their power means that they have been right all along. Skeptically identifying the best-known speakers, thought leaders, and actors in the movement in favor of UBI is how Cass builds his credibility with his audience.

In her book, Rhetorical Criticism – Exploration and Practice, Sonja K. Foss devotes a chapter to what she labels Ideological Criticism, described as “the privileging of the ideology of one group over the ideologies of other groups… that represents experience in ways that support the interests of those with more power” (Foss, 2018 p. 239). This is an obviously accurate description of the methodology Cass’s white paper deploys to argue his position. Society’s belief in his version of social norms and values must be maintained or else his argument unravels into a selfish, greedy, and isolated position that reminds me of Shelley’s poem Ozymandias written in 1817 that describes a statue of the world’s most powerful leader centuries later, now decrepit as “two vast and trunkless legs of stone stand/in the desert” (Shelley, 2002). Only the legs remain, not the body, like an argument that needs a powerful body but has only the legs to move the once powerful assertions that have been diminished by time.

In addition to using norms and values to build his arguments, Cass leverages two additional techniques Foss calls Hegemonic Ideology, “position and group relations” and “ultimate authority” (Foss, 2018 p. 238). The former concept focuses on the audience as “supporters of the group members” in relation to “their enemies or opponents” while the latter asks, “what is the sanctioning agent” with the power to arbitrate what is true, or should be excluded from the argument (Foss, 2018 p. 238). The implied racism and obvious classism inherent in several of Cass’s arguments attempt to redirect factual arguments presented by eminent scholars and productive businesspeople into a narrow discussion of unsubstantiated, idealized conservative opinions of history as it relates to current affairs today. To a liberal perspective, this redirection is a necessary function of conservative discourse because the basis of their arguments is a coercive misrepresentation of historical facts necessary to support the dominant ideology. Facts matter, but not to Cass. In the following pages, I will leverage these rhetorical techniques to unravel the prejudiced, classist, elitist, and wrong-headed essay of an archconservative who is far more concerned with maintaining his white privilege than he is with discussing the merits of the arguments about progressive proposals that attempt to solve the problem of unemployment, and the related issue of poverty, through an income redistribution program named UBI.

Analysis

The central idea of his essay has been effectively conveyed by the title, “Why Universal Basic Income is a Terrible Idea” (Cass, 2016, pg. 1) but it reflects the core weakness of his argument, which requires a sympathetic audience and as such, does not even attempt to engage in persuasive arguments based on analysis, anecdote, or data. Cass elaborates on his central idea using a flamboyant, one-line paragraph to begin the white paper, “UBI would only entrench our misconceptions about the relationship between the individual and the state” (Cass, 2016, pg. 1) previewing his ideologically focused argument against wage replacement and social welfare benefits. He does not write for an audience of policy makers who require analysis to make decisions, but for those who are motivated by ideology and their memory of the past. Unfortunately for his argument, those memories are often incomplete and extremely limited because they include only personal perspectives.

Without the more complex and nuanced objective viewpoints, his arguments can be more readily consumed by an audience who observe the discourse but do not directly participate in creating the policy response it enacts. Cass establishes his pathos by presenting a roll call of eminent think-tank writers, politicians, and economists, positioning himself as credible because he has knowledge of the issue’s current arguments. After introducing of the idea’s promoters like the chorus in a Grecian drama, “Columnists,” followed by two more groups that he names inside quote marks as “’data journalists’” and “’explainers’” he ends the list by naming the group that stands to provide the most benefit to society while also being a symptom of the unemployment problem being solved by UBI, “technologists” (Cass, 2016, pg. 1). Cass intends his use of quotes around the categorical names of his opponents to instill a skepticism of those groups in his audience. It’s his way of implying that ‘so-called’ or ‘supposed’ should precede their names because they are not part of his, and his audience’s, group.

He must believe that his group is disinterested in facts but intrigued by statements that sound impressive because he uses weak data to support his position that a UBI will be cost prohibitive. The second paragraph introduces an elementary math error when estimating the cost per individual for UBI, “a monthly check of $800 or $1,000 to cover basic needs… a couple would receive $20,000 per year” (Cass, 2016, p. 1). It’s obvious that monthly checks at those amounts would be $9,600 and $12,000, not $20K, per year but the intention of his argument is not to be objective, rational, or accurate. Instead, he intends to make arguments with which his audience is already familiar because they are part of a social class and ethnic group that already fears any benefit that may be given to someone other than themselves. I agree with Foss’s point about ideological rhetoric that states a “dominant ideology controls what participants see as natural or obvious by establishing the norm” (Foss, 2018 p. 239) and the sole data-driven example in Cass’s paper attempts to establish a false fact that will be meaningful to his audience because it sounds good, without any regard for the accuracy of the basic math.

Following a cost estimate that has been inflated transparently, Cass transitions to the body of his argument through a ‘They Say/I Say’ that refutes a progressive labor secretary by implying that he has presented a false obligation. “Former labor secretary Robert Reich, plugging Stern’s effort, says, ‘America has no choice.’ Actually, we do have a choice — one that goes far beyond safety-net details to reach the very heart of state and society” (Cass, 2016, p. 2). The argument against Reich is not followed by justification for an alternative opinion, but instead, the enthymeme is the conservative reverence for a secular state with heart, based on the historical values of an oppressive Caucasian majority. He reminds his audience of everlasting American norms and values, while naming his opponents as defenders of a social safety-net that is detrimental to the current moral values embodied in American society, as he sees it.

As Foss explained, hegemonic ideology privileges one group above another. Cass casts his opponents as breakers of the societal covenant that he believes had been established by the prior century. For Cass, values and norms should not change. He names government as becoming a provider if UBI is enacted, implying without evidence that it will be a new role for the government. Just a paragraph earlier though, he had referenced the “safety-net programs that would no longer be necessary” but now contradictorily states that government does not provide for “individuals, families, or communities” (Cass, 2016, p. 2). Like the elementary math error that initiates his argument, contradicting himself on the same page is not a concern while he reinforces an ideological distrust of facts and logic.

Cass reminds his readers about the racist and classist theories of the past, positioning the United States as a country with two sides: “the Haves” and “the poor and the black” the latter being the beneficiaries of policies “that have absolved people of responsibility for themselves and one another… and thereby eroded the foundational institutions of family and community that give shape to society” (Cass, 2016, p. 2). Cass’s narrow point of view built on racist principles created to maintain wealth and power amongst a few citizens are his replacement for concrete evidence to support his central idea. Campbell’s points to this directly when she labels an audience as a “reenergized citizenry committed to carrying on the fight” (Campbell, 2013, p.30). By grouping those in poverty with the black community, he attempts to ostracize both groups with a norm-breaking label that implies that they have not taken responsibility for themselves or their society. Like his use of skeptical quotes to label his detractors as ‘so-called’ and unworthy, Cass uses blatantly racist tropes to define his opposition to social welfare programs while attempting to establish his argument and his group as the ultimate authority.

He claims that “unfulfilling” work is nonetheless imbued with meaning, which supports a conservative view of family as male-dominated, which he emphasizes by referring to the worker as a “breadwinner” who provides essentials for their family, whereas UBI would eliminate work’s “essential role as the way to earn a living, work would instead be an activity one engaged in by choice, for enjoyment, or to afford nicer things” (Cass, 2016, pg. 4). Yet work is already these things for some people at all levels of the wage scale, especially those who have earned higher wages in the past. I am an example of this type of worker, who has worked hard and sacrificed to generate enough savings through income to be able to respond to unfulfilling employment by making a life change that I believe will lead to more rewarding work. Mark Zuckerberg and other billionaires talk about how a secure financial safety net would have enabled them to invest more energy and time into their big ideas, so that the benefits to society would have been delivered sooner. “We should have a society that measures progress not by economic metrics like GDP but by how many of us have a role we find meaningful” (Zuckerberg, 2017, 18:00). Wealth as the result of innovation is a net-positive to Cass, but his ideology refuses to accept the societal changes that may accompany it, most notably when there is a cost to his audience’s wealth. Zuckerberg’s speech, most progressive discourse, and my own experience all acknowledge that work is not only essential, but also enjoyable and a choice determined by bigger factors than a need for more expensive things.

Finally, Cass tries to act as the agency that sanctions what is right or wrong in the personal lives of low-income workers. He restates the Luddite Fallacy that technological progress leads to more opportunity for work. He assumes that Farm labor transitioned into Service labor. That may or not be true, but this year we have seen how providing wage support funding during the pandemic has altered the loyalty of Service workers of America to their exploitative and now potentially health-damaging employment. As reported by the Chicago Tribune, “Americans quit their jobs at a record pace for the second straight month in September” (Rugaber, 2021). Workers who are changing jobs at the highest rate in the history of America most often cite dissatisfaction and risk to health as the reason. The relatively small additional wages provided to these workers to offset job loss during the pandemic has enabled them to make a choice to select a new job with better incentives for them to work. They are not selecting the option that Cass suggests will be the outcome of UBI – i.e., not working.

As he presents final arguments against providing benefits that are not tied to work programs for low-income workers, Cass uses Charles Murray’s well-known classist, misogynistic argument about the difference in intact nuclear families between 1960 – a time in history idealized by 21st Century Conservatives – and the early 2000s. His data reports that in 1960 more than 95% of children lived with two parents when mom was 40 years old, but that has declined to 60% in this century. He quotes Murray several times, reminding his audience “that it calls into question the viability of white working-class communities” (Cass, 2016, pg. 6). The difference in what he labels as intact households is more likely related the fact that stress caused by unfulfilling work is a major contributor to domestic abuse according to The American Psychological Association (APA). Guidelines for domestic abuse noted that “individuals living with LIEM [Low Income and Economic Marginalization] may suffer from increased mental health symptoms and mental health disorders; limited opportunity for engaging in healthy behaviors; and a decreased capacity to manage stressors both cognitively, mentally and socially” (Parker, 2019). Cass avoids addressing these challenges faced by LIEM households and the finding by the APA, which is indicative of a fundamental problem with the Conservative mindset that a married woman is necessarily better off.

In conclusion, Cass’s white paper “Why a Universal Basic Income Is a Terrible Idea” provides an example of a Conservative American pundits who knows his audience well. The fact-lessness of his, and his audience’s, hegemonic ideology leverages weak arguments, elementary math errors, generalized and counterfactual labels for opposing voices, and overt racism to convey it’s elitist argument. According to Foss (2018), this “constitutes a kind of social control, a means of coercion, or form of domination by more powerful groups over the ideologies of those with less power” (p. 239). The values and norms of the group in power – often defined as the group with the most wealth and the most wealth to lose via redistribution policies – demand an ideology that assesses their beliefs as good and the opinions of those who disagree as contrary to society. Asserting an ultimate authority, his statistics do not need to be corroborated, nor allow any broad context that would complicate the stated opinion of his group. Despite his reliance on the principle that a citizenry can aggressively defend its ideals in the face of overwhelmingly oppositional facts, I am grateful for the ability of a citizenry to mobilize into action, to innovate for the benefit of all society and all workers, for data and anecdotes that can be validated and thus used to make better decisions about how to support workers through times of unemployment; for example, an income insurance program like UBI.

 
References

• Campbell, K. et al. (2013) The Rhetorical Act: Thinking, Speaking and Writing Critically Cengage Learning.
• Cass, O. (2017, March 21). Why a universal basic income is a terrible idea. Manhattan Institute. Retrieved November 15, 2021, from https://www.manhattan-institute.org/html/why-universal-basic-income-terrible-idea-8984.html.
• Foss, S. K. (2018). Rhetorical criticism: Exploration and practice. Waveland Press, Inc.
• Parker, S. (2019, December). Living with Low-Income and Economic Marginalization (LIEM)— How can psychologists help? The SES Indicator. http://www.apa.org/pi/ses/resources/indicator/2019/12/low-income-marginalization
• Rugaber, C. (2021, November 12). Americans quit their jobs at record pace for 2nd Month. chicagotribune.com. Retrieved November 30, 2021, from https://www.chicagotribune.com/business/ct-biz-americans-quit-jobs-record-pace-september-20211112-phzh7bkqmfbthkq6nks242np2u-story.html.
• Shelley, P. B., Reiman, D. H., & Fraistat, N. (2002). Shelley's poetry and prose: Authoritative texts, criticism. Norton & Company.
• YouTube. (2017). Facebook Founder Mark Zuckerberg Commencement Address | Harvard Commencement 2017. Retrieved November 21, 2021, from https://www.youtube.com/watch?v=BmYv8XGl-YU.

 
 ]]>Wed, 10 Nov 2021 08:00:00 GMThttp://matthewtfrazier.me/blogs/creativity-and-intelligence-in-applied-educational-psychology-post-5Module 5 – Developmental and Environmental

1. Piaget and Vygotsky – cognitive development models (enviro. crucial)
2. Aging – Horn study and Shimamura et al Study – what changes, what protects?
3. Cultural influences – Luria (formal ed.), Kearin (spatial reasoning), Bart (proportional reasoning)
4. ​Home Influences – Dave, and also Wolf (at-home enrichment); Rauscher (musical)

I appreciated that our lecture notes started with the example of Piaget applying his cross-domain knowledge – a critical component of successful, creative teams – using a principle of biology to address a concern of educational psychology. My first finding was the clear evidence from the Luria study that clearly illustrated the relationship between categorical knowledge and education. It was a surprise to see that the responses to her questions in the study were as literal and functionally oriented as his hypothesis expected. Responses such as “plate” when being asked about a flat round object, declining to answer the question about Socrates due to a lack of direct knowledge of the person, and the avoidance of such an open-ended invitation to ask about any subject, made it clear to me that their mental models were the formed by their environment – just as those with greater education were clearly affected by their time in the school environment. My path to becoming a language arts and technology teacher has been strongly influenced by a desire to strengthen abstract thinking skills in young people, to help them synthesize their domain knowledge and apply it effectively in new situations, and this study is a great example of the potential long-term impact of education on cognitive skills, notably the abstract.

Dave’s study in particular, but also Wolf’s study, was personally meaningful to me, my second finding. I grew up as the only child of a single mother – an English teacher who was underpaid – and we struggled through financial and social disadvantages in the upper-middle class neighborhood she insisted had to be our home. Many weekend mornings, we would take the bus into New York City to spend the day in the museum. We would see the second half of Broadway shows, ballet, or the symphony because admission was free if you waited until intermission to enter. She would say that half-a-show was still plenty of enrichment time. Time at home was often oriented toward similar activities and reading. Wolf’s discovery of the high correlation between his rating of their home environment and their score on the Henmon-Nelson IQ test provides some explanation for why I was a successful student at an early age even though I had not participated in the same pre-school enrichment programs as my peers. Dave’s secondary finding about reading and word knowledge were also true for me as a young boy.
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Reading about the cognitive epidemiology data that showed a significant mortality risk difference based on intelligence was another finding. The overall difference was not surprising, but the more than 200% higher mortality rate when comparing the highest to the lowest ends of the index was striking. In this module more than the others, I noticed that I was considering the influence of external factors while reading about the findings of studies, or the points being made by Ritchie. Social class and genetics, the influence of family, must be relevant; successful parents share their lessons of success with their children. Ritchie’s addressed this well in the summary at the end of the chapter by reminding us that while intelligence is one aspect of being human that has a strong psychological impact, it’s not the only explanation for the results of the studies. Finally, I had always suspected that intelligence and near-sightedness were related and he provided evidence that that was true.]]>