Algorithms of Oppression by Safiya Umoja Noble

Hardcover, Expected pub: Feb 20th 2018 by New York Univ Press, ISBN13: 9781479849949

Noble began collecting information in 2010 after noticing the way Google Search and other internet sites collect and display information about non-white communities. Her results dovetail with other work (e.g., Weapons of Math Destruction by Cathy O'Neil) positing that algorithms are flawed by their very nature: choosing & weighting only some variables to define or capture a phenomenon will deliver a flawed result. Noble wrote this book to explore reasons why Google couldn’t, or wouldn’t, address concerns over search results that channel, shape, distort the search itself, i.e., the search “black girls” yielded only pornographic results, beginning a cascade of increasingly disturbing and irrelevant options for further search.

In her conclusion Noble tells us that she wrote an article about these observations in 2012 for a national women’s magazine, Bitch, and within six weeks the Google Search for “black girls” turned up an entire page of results like “Black Girls Code,” Black Girls Rock,” “7-Year-Old Writes Book to Show Black Girls They Are Princesses.” While Noble declines to take credit for these changes, she continued her research into the way non-white communities are sidelined in the digital universe.

We must keep several things in mind at once if the digital environment is to work for all of us. We must recognize the way the digital universe reflects and perpetuates the white male patriarchy from which it was developed. In order for the internet to live up to the promise of allowing unheard and disenfranchised populations some voice and access to information they can use to enhance their world, we must monitor the creation and use of the algorithms that control the processes by which we add to and search the internet. This is one reason it is so critical to have diversity in tech. Below find just a few of Noble's more salient points:

※ We are the product that Google sells to advertisers.

※The digital interface is a material reality structuring a discourse, embedded with historical relations...Search does not merely present pages but structures knowledge...

※ Google & other search engines have been enlisted to make decisions about the proper balance between personal privacy and access to information. The vast majority of these decisions face no public scrutiny, though they shape public discourse.

※ Those who have the power to design systems--classification or technical [like library, museum, & information professionals]--hold the ability to prioritize hierarchical schemes that privilege certain types of information over others.

※ The search arena is consolidated under the control of only a few companies.

※ Algorithms that rank & prioritize for profits compromise our ability to engage with complicated ideas. There is no counterposition, nor is there a disclaimer or framework for contextualizing what we get.

※ Access to high quality information, from journalism to research, is essential to a healthy and viable democracy...In some cases, journalists are facing screens that deliver real-time analytics about the virality of their stories. Under these circumstances, journalists are encouraged to modify headlines and keywords within a news story to promote greater traction and sharing among readers.

An early e-version of this manuscript obtained through Netgalley had formatting and linking issues that were a hindrance to understanding. Noble writes here for an academic audience I presume, and as such her jargon and complicated sentences are appropriate for communicating the most precise information in the least space. However, for a general audience this book would be a slog, something not true if one listens to Noble (as in the attached TED talk linked below). Surely one of the best things this book offers is a collection of references to others who are working on these problems around the country.

The other best thing about this book is an affecting story Noble includes in the final pages of her Epilogue about Kandis, a long-established black hairdresser in a college town trying to keep her business going by registering online with the ratings site, Yelp. Noble writes in the woman’s voice, simply and forthrightly, without jargon, and the clarity and moral force of the story is so hard-hitting, it is worth picking up the book for this story alone. At the very least I would recommend a TED talk on this story, and suggest placing the story closer to the front of this book in subsequent editions. For those familiar with Harvard Business Review case studies, this is a perfect one.

Basically, the story is as follows: Kandis's shop became an established business in the 1980s, before the fall off of black scholars attending the university "when the campus stopped admitting so many Blacks." To keep those fewer students aware that her business provided an exclusive and necessary service in the town, she spent many hours to find a way to have her business come up when “black hair” was typed in as a search term within a specified radius of the school. The difficulties she experienced illustrate the algorithm problems clearly.

“To be a Black woman and to need hair care can be an isolating experience. The quality of service I provide touches more than just the external part of someone. It’s not just about their hair.”

I do not want to get off the subject Noble has concentrated on with such eloquence in her treatise, but I can’t resist noting that we are talking about black women’s hair again…Readers of my reviews will know I am concerned that black women have experienced violence in their attitudes about their hair. If I am misinterpreting what I perceive to be hatred of something so integral to their beings, I would be happy to know it. If black hair were perceived instead as an extension of one’s personality and sexuality without the almost universal animus for it when undressed, I would not worry about this obsession as much. But I think we need also to work on making black women recognize their hair is beautiful. Period.

By the time we get to Noble’s Epilogue, she has raised a huge number of discussion points and questions which grew from her legitimate concerns that Google Search seemed to perpetuate the status quo or service a select group rather than break new ground for enabling the previously disenfranchised. This is critically important, urgent, and complicated work and Noble has the energy and intellectual fortitude needed to work with others to address these issues. This book would be especially useful for those looking for an area in the digital arena to piggyback her work to try and make a difference.

Below please find a 12-minute TED talk with Ms. Noble:



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The Undoing Project by Michael Lewis

This nonfiction is unlike others Michael Lewis has offered us. In this he tries the trick of explaining confusion by demonstrating confusion, but near the end of this work we appreciate again Lewis’ distinctive clarity and well-developed sense of irony as he addresses a very consequential collaboration in the history of ideas. Lewis did something else he’d not done before as well. By the end of this book I was bawling aloud, in total sync with what Lewis was trying to convey: why humans do what we do.

Daniel Kahneman is a psychologist who won the 2002 Nobel Prize in Economics. What is remarkable about that statement is also what is remarkable about Lewis’ attempt to explain it. Lewis made us feel the chaos and the unlikelihood of such a success, in this case, of ever finding that one person who complements another so perfectly that the two literally spur one another to greater accomplishment. From a vast array of possible choices, opportunities, and directions come two psychologists, Daniel Kahneman and Amos Tversky, who together add up to more than the sum of their parts.

One thing became clear about the groundbreaking work done by Kahneman and Tversky: despite the curiosity, drive, and iconoclastic talent each possessed, their moments of greatest crossover relevance came as a result of the involvement of the other. This could push the discussion into an examination of the importance of pairs in creativity, but Lewis resists that thread to follow what he calls a “love story” to the end, to the breakup of the two men. Once the closest of friends and collaborators, the reason for their breakup is as instructive as anything else Lewis could have chosen to focus on, and it makes a helluva story, full of poignancy.

Kahneman was an idea man, throwing up new psychological insights constantly, beginning with his early work recruiting and training Israeli soldiers for the front line. Tversky was a widely admired mathematical psychologist, iconoclast, and skeptic who challenged accepted thinking and in so doing, provided new ways to look at old problems. Just by asking questions he could lead others to find innovative answers. Both Israelis were teaching at the University of Michigan in the 1960s but their paths didn’t overlap until later, back in Israel. In one of the classes he taught at Hebrew University Kahneman challenged guest lecturer Tversky’s discussion on how people make decisions in conditions of uncertainty.

In this instance Kahneman became the iconoclast, the skeptic, pulling the rug from underneath Tversky. The challenge got under Tversky’s skin, but instead of falling prey to anger, Tversky was galvanized. Colleagues who saw him at this time recall his unusually intense period of questioning. Again, after a period of time, the men came together again, and thus began one of the richest and most rewarding periods of intellectual collaboration in modern times.

Together, both men were able to isolate some important pieces in the thinking sequences of humans who were presumed to maximize utility in rational, logical decision trees. It took many years to isolate what struck them as incomplete or incorrect in the accepted thinking of others, but what they concluded revolutionized the thinking in several disciplines, including economics (and baseball).

Lewis’s earlier book Moneyball: The Art of Winning an Unfair Game discussed how an algorithm assigning different weights to individual characteristics of baseball draft picks could by-pass the errors human tend to make when looking over a list of potential players. This is related, in a distant way, to the illogic discovered in the decision trees Kahneman and Tversky discussed, and unfortunately Lewis decides to revisit the breakthrough in his own understanding at the beginning of this book. Describing that tangential result of the men’s essential discovery unnecessarily complicates and obfuscates Lewis’ central thrust in this book—the relationship between two men who supercharge their achievements when they are together. Once Lewis settles into the real subject of his book, his writing becomes familiarly crystalline, filled with science and emotion, describing a singularly fascinating tale.

Particularly interesting is Lewis’ attention to how ideas develop. Lewis tries in several instances to get to the moment of insight, and then to the moments of greater insight which might lead finally to upturning accepted beliefs about how one thinks the world must work. Happiness and regret both came under the microscopes of these men and it was hugely insightful for them to discover that regret was the more impelling emotion. People often made decisions to minimize regret rather than maximize happiness. This led to the ‘discovery’ that the value of positive ‘goods’ decreased after a certain level of attainment, while the value of negative ‘bads’ never lost their bite. Which could be another way of describing the apparently supreme need to minimize loss rather than maximize gain. Which led to the discovery that people often gamble against what had been perceived as their own interests.

The two men were opposites of one another, Kahneman a heavy smoker whose office was messy and disordered, and Tversky, who hated smoking, had an office so well-organized it looked empty. For a period of almost twenty years, during the years of their greatest output, they could often be found together, talking, or writing one another if apart. They published hugely influential papers and became the toast of several continents. The closeness of the two men appeared to have no discrepancy until gradually over time, Tversky became better known and more popular in scientific and academic circles. The equilibrium of the relationship was thus unbalanced and a period of estrangement led the men in different directions.

The entire story, in Lewis’ hands, is wonderfully moving. If you can thrash your way through the thicket of ideas at the entrance to the main repository of ideas in this book, prepare yourselves to be utterly delighted.




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Weapons of Math Destruction by Cathy O'Neil

Hardcover, 259 pgs, Pub Sept 6th 2016 by Crown, ISBN13: 9780553418811, Lit Awards: Goodreads Choice Award Nominee for Science & Technology (2016)

O’Neil deserves some credit right off the bat for not waiting until her retirement from the hedge fund where she worked to tell us the secrets of how corporations use big data (our data). Underlying the collection and use of big data is an attempt to utilize efficiencies in the market place for goods, money, and talent. Big data ostensibly can also “set us free” from time constraints and uneven knowledge dispersal. Conversely the opposite is often true. We are at the mercy of how our own data is shredded and packaged, and errors in the model can mean mutually assured destruction—for the school, corporation, family.

The book starts with examples any readers who actually picked up this book to read might recognize: the chances of getting into a major university. O’Neil doesn’t go into the actual algorithms but just explains the variables chosen to populate the algorithms. Just when I was wondering who this book is targeted at, since after all, we kind of know how to get into university already, she comes up with examples of big data messing with aspirations that are still (hopefully not) in our futures.

She addresses the real pain-in-the-ass nature of minimum wage jobs where the inadequate part-time hours are constantly changing to maximize profits for owners and to screw with employees ability to plan their life, their children’s lives, and the children’s caretaker’s lives. O’Neil addressed the situation in 2009 when Amex decided to reduce the risk of credit card nonpayment by reducing the credit ceilings on users who shopped at certain stores, like Walmart. She shows us the way micro-targeting ends up using data to perpetuate inequities in opportunity and “social capital.”

The hardest part of reading this book (there is no actual math), was keeping my mind on what O’Neil was saying. Every time she'd mention another example of the ways big data was screwing us over, my mind would wander to experiences of my own, or ones I’d heard from friends, family, or others. This is real stuff, and just when I thought that it would be an excellent book for those with skills and interest in social justice to take to an interview with Google, Amazon, or a big bank, in she comes with another example of how the “fixes” are almost worse than the disease (Facebook’s method of who your friends are determining your credit risk).

But O’Neil reminds us big data, mathematics, algorithms, etc. aren’t going to go away.

"Data is not going away. Nor are computers—much less mathematics. Predictive models are, increasingly, the tools we will be relying on to run our institutions, deploy our resources, and manage our lives. But as I’ve tried to show throughout this book, these models are constructed not just from data but from the choices we make about which data to pay attention to—and which to leave out. Those choices are not just about logistics, profits, and efficiency. They are fundamentally moral."

Exactly. We still have to use our brains, not just our computers. It is critical that we inject morality into the process or it will always be fundamentally unfair in some way or another, especially if the intent is to increase profits for one entity at the expense of another. One simply can’t include enough variables or specifics. Some universities have begun to audit the algorithms—like Princeton’s Transparency and Accountability Project—by masquerading as people of differing backgrounds and seeing what kind of treatment these individuals receive from online marketers.

O’Neil suggests that sometimes data might be used to good effect by targeting frustrated online commenters with solutions to their issues: i.e., affordable housing info, or by searching out possible areas of workplace or child abuse and targeting that area with resources. She wades into national election data and notes that only swing states get candidates attention, suggesting, by the way, that the electoral college has outlived its usefulness to the citizenry. Algorithms are not going to administer justice or democracy unless we find a way to use them as a tool to root out inequities and try to find ways to deliver needed services where they are deficient.

When I look at the totality of what O’Neil has discussed, I am inclined to think this book is best targeted to thoughtful high schoolers and college-aged students who are thinking about planning their careers, who have a penchant for mathematical and computer modeling, and who think their dream job might be with an online giant. I’d be happy to be disabused of this notion if someone wants to challenge my thought that much of this information is known to many of us who have been out of school for awhile and who have been paying attention to our online experiences and junk mail solicitations. But it is always interesting to read someone as coherent and on the side of social justice as Ms. O’Neil.

It might be noted that Jaron Lanier in Who Owns the Future? (2013) also talks about the use of big data to steer our thinking and makes a preliminary suggestion that individuals should be paid for their data—for data that is collected about them, for profit. It is an interesting discussion as well. Love these intersections of technology and humanity.



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A Field Guide to Lies: Critical Thinking in the Information Age by Daniel J. Levitin

Oh, boy, I wish every one of my fellow citizens had the information shared in this book as part of their reading regime. On one hand, it would make it much harder to convince people with statistics. On the other hand, it would be much harder to convince people with statistics. Come to think of it, I think nowadays most people mistrust statistics, unless the statistics back up their own opinion.

How many times I received end-of-quarter reports from some mutual fund company showing showing growth and profits exceeding other companies’ but their graphs do not have the axes on their bar charts or line graphs labelled. Even one so discrepant in the moneymaking arts as I know this for a sham report.

Levitin does a couple of things in this book: he describes common ways to use statistics to disguise facts. He points out common errors the best-intentioned of us make (like doctors determining probabilities in positive cancer screens) and leads us to the way to find answers. He demystifies “expert testimony” by pointing out that expertise is typically narrow.

Donald Trump features in this book, both quoted directly and by implication:

“Truth is the default position and we assume others are being truthful with us. An old joke goes, “How do you know that some is lying to you? Because they begin with the phrase to be perfectly honest. Honest people do not need to preface their remarks this way.”

In the last third of the book, Levitin tells us how to think straight: deduction and induction, logical fallacies, framing risk, and belief perseverance, ending with a separate chapter on Bayesian probability. Finally, he gives four case studies to see if you managed to understand what he’d been telling you all along. He ends with a physicist’s explanation of new ideas and what we really don’t know for sure.

Levitin is very good. The material in his book parallels an earlier book I’d reviewed, Psy-Q by Ben Ambridge, which takes a fun look at the ways we can deceive or stun our friends. And, truthfully (?!), I found Ambridge's explanation of Bayesian probabilities a little more understandable and applicable. But if you are like me, you need to review those proofs again and again every which way before you can explain it yourself. Psy-Q is a Penguin Paperback Original.

Both these books would be very useful for high school or college students or educators. These experts (now I wonder if I can use that term ever again) try to make it easy for us whose expertise lies elsewhere. It seems that most Americans may have learned only half of what they needed to from this book, so learning what we didn’t the first time around will be useful for the rest of our lives.

Below a short video intro by Levitin explaining logical fallacy which will give you some idea of the audience to whom he is speaking:




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Things to Make and Do in the Fourth Dimension by Matt Parker

Matt Parker is a comedian who does stand-up math. Or he is a mathematician doing stand-up comedy…I forget which lifestyle definition attracted me to his routines on YouTube: some are complicated enough to make you forget to laugh…unless, that is, you are already in on the math basics he is sharing. I learned about Parker’s new book from the mathematician Ben Babcock, whose website reviews recently-published science fiction, among other things. I was impressed with his assessment that “this is DIY math at its finest”-- impressed enough, after looking at it myself, to buy copies for my teenaged nephews.

Besides that, in the YouTube clip I saw, Parker is wearing maths paraphernalia like a “smooth geometric t-shirt” sold by DESIGNBYHÜMANS that is über-cool for mathheads.

I like to encourage thinking and innovation of any kind.

Parker doesn’t neglect important relevant applications of mathematics: how to cut a pizza equally with crust or without, how best to keep your headphone wires from tangling, how to tie your shoes (!) the maths way…in other words, ways to learn and test math principles using everyday objects…or your classroom full of students. It actually does sound fun, which I guess is the point. Babcock makes it clear that one really understands maths by doing math, which is perhaps even more to the point.

Below is a clip of one of Parker's routines.



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Psy-Q by Ben Ambridge

I may have had an unusual education, but by the time I left college I did not know that many companies administer a type of IQ test or personality test to applicants as part of their interview procedure. Only in a course in graduate school did I encounter the very cool questions devised to see how one thinks. Ben Ambridge doesn’t give us many IQ questions here, but lots of PSY-Q questions, designed to determine how people perceive, think, and compose opinions. Ambridge thinks they’re fun, and I agree, but they’re not only fun. I argue that it is also instructive to know how most people answer these questions, right or wrong.

The set up for the puzzles, jokes, experiments might be just a sentence or a paragraph. The explanation often takes a little more space, not including thinking time. Take for example the short set up for The Patient: “Scientists have found a new disease that is spreading around the country…The disease is pretty rare, but it causes cancer…scientists have developed a test that is 99% accurate, and you have tested positive! What are the chances you have the disease?” I am sure you have seen this, or a variation of this example before. Do you remember how to solve it? What percentage of folks can figure it out? (A hint: many psychologists find this confusing!) Ambridge gives us this, a little history of how the question is used in real life situations, and links to further reading about game theory, examples, and a math website that makes jokes about frequent errors in the use of statistics.

Ambridge also uses real life scenarios like online dating statistics, whether or not to leave your present job with a struggling company, whether to change lanes in heavy traffic. I have encountered these types of questions, the results, and the studies that engendered them before but Ambridge is such a good-humored and enthusiastic host that one doesn’t mind looking the fool once again. Truthfully, I think this is the perfect book for a bright teen who may find they are interested in the way folks make decisions, reveal their prejudices, and believe fallacies. Our own errors in judgment are likewise illustrated.

And not just teens! My brother just returned from a job interview for a high-end managment job and they gave him a PSY-Q for TWO HOURS! What a riot. I wish I'd shown him the book beforehand. He at least wouldn't have been surprised or thrown by some of the questions. Well then, perhaps teens also. The teen with whom I shared the book with became immediately engrossed. He’d stated more than once that he might be interested in programming for online games. I can’t think of a more entertaining way to learn about the ways people perceive the information they are given, how they react in certain situations, and how impressionable we all are. This book is a fun way to get an education.

Ambridge is amusing, clear, and relevant. Readers may find they want to follow the web links to further information and more tests, if their interest is piqued.

This book is a Penguin Paperback Original, but it is also available as an eBook. If the links are embedded in the e-text, that might be the best way to read this book, though there is something about being able to pass around a paper copy that is appealing.


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