For the #e21sym question about Amazon’s AI discriminating against women, we do have some control. This article about how Sears helped to provide equality in the Jim Crow south is a great historical example of how economics can create equality: https://boffosocko.com/2018/10/18/searss-radical-past-how-mail-order-catalogues-subverted-the-racial-hierarchy-of-jim-crow-washington-post/
In a special bonus episode of the podcast Crazy/Genius, the computer scientist and data journalist Meredith Broussard explains how “technochauvinism” derailed the dream of the digital revolution.
I was excited to hear Dr. Meredith Broussard, a brilliant colleague I’ve met via the Dodging the Memory Hole series of conferences, on this podcast from The Atlantic. I would recommend this special episode (one of their very best) to just about anyone. In particular there’s something to be gained in the people side of what the IndieWeb movement is doing as well as for their efforts towards inclusion.
From a broader perspective, I think there’s certainly something to be learned from not over-sensationalizing artificial intelligence. Looking at the history of the automobile as a new technology over a century ago is a pretty good parallel example. While it’s generally done a lot of good, the automobile has also brought along a lot of additional societal problems, ills, and costs with it as well.
I hadn’t yet heard about her new book Artificial Unintelligence: How Computers Misunderstand the World which I’m ordering a copy of today. I suspect that it’s in the realm of great books like Cathy O’Neill’s Weapons of Math Distraction: How Big Data Increases Inequality and Threatens Democracy which was also relevant to some of the topics within this podcast.
There are many things that matter that we don’t always see from an individual perspective. We also simultaneously need to be careful of attempting to only see things in the aggregate.
Originally bookmarked to watch on September 28, 2018 at 09:17AM. Missed the live stream due to time zone differential.
Behind the landmark Supreme Court ruling of Brown v. Board of Education was a girl named Linda Brown, whose story led to states being ordered to desegregate schools, mostly against their will. Ms. Brown died on Sunday. Who was she, and what has changed in the nearly 64 years since the case was decided?
On today’s episode:
• Nikole Hannah-Jones, an investigative reporter covering race and civil rights for The New York Times Magazine.
• The New York Times obituary of Linda Brown.
A fantastic piece of journalism here. Timely, interesting, and important. This is the type of coverage that keeps me coming back to The Daily on a regular basis.
Obituaries in The New York Times have been long dominated by white men. We’re adding the stories of remarkable women like Ida B. Wells, who took on racism in the South.
Some nice pieces of history here that I’m sad to say I hadn’t heard about and didn’t know they were as egregious as I had thought. I knew about lynchings in general, but didn’t know that they rose to a level as high as the one described here.
Women in Harvard's math department report a bevy of inequalities—from a discouraging absence of female faculty to a culture of "math bro" condescension.
A story about math that sadly doesn’t feature equality.
Oddly not featured in the story was any reference to the Lawrence H. Summers incident of 2005. Naturally, one can’t pin the issue on him as this lack of diversity has spanned the life of the university, but apparently the math department didn’t get the memo when the university president left.
I’ve often heard that the fish stinks from the head, but apparently it’s the whole fish here.Syndicated copies to:
📖 Read chapter one of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil
I don’t think she’s used the specific words in the book yet, but O’Neil is fundamentally writing about social justice and transparency. To a great extent both governments and increasingly large corporations are using these Weapons of Math Destruction inappropriately. Often it may be the case that the algorithms are so opaque as to be incomprehensible by their creators/users, but, as I suspect in many cases, they’re being used to actively create social injustice by benefiting some classes and decimating others. The evolving case of Facebook’s involvement in potentially shifting the outcome of the 2016 Presidential election especially via “dark posts” is an interesting case in point with regard to these examples.
In some sense these algorithms are like viruses running rampant in a large population without the availability of antibiotics to tamp down or modify their effects. Without feedback mechanisms and the ability to see what is going on as it happens the scale issue she touches on can quickly cause even greater harm over short periods of time.
I like that one of the first examples she uses for modeling is that of preparing food for a family. It’s simple, accessible, and generic enough that the majority of people can relate directly to it. It has lots of transparency (even more than her sabermetrics example from baseball). Sadly, however, there is a large swath of the American population that is poor, uneducated, and living in horrific food deserts that they may not grasp the subtleties of even this simple model. As I was reading, it occurred to me that there is a reasonable political football that gets pushed around from time to time in many countries that relates to food and food subsidies. In the United States it’s known as the Supplemental Nutrition Assistance Program (aka SNAP) and it’s regularly changing, though fortunately for many it has some nutritionists who help to provide a feedback mechanism for it. I suspect it would make a great example of the type of Weapon of Mass Destruction she’s discussing in this book. Those who are interested in a quick overview of it and some of the consequences can find a short audio introduction to it via the Eat This Podcast episode How much does a nutritious diet cost? Depends what you mean by “nutritious” or Crime and nourishment Some costs and consequences of the Supplemental Nutrition Assistance Program which discusses an interesting crime related sub-consequence of something as simple as when SNAP benefits are distributed.
I suspect that O’Neil won’t go as far as to bring religion into her thesis, so I’ll do it for her, but I’ll do so from a more general moral philosophical standpoint which underpins much of the Judeo-Christian heritage so prevalent in our society. One of my pet peeves of moralizing (often Republican) conservatives (who often both wear their religion on their sleeves as well as beat others with it–here’s a good recent case in point) is that they never seem to follow the Golden Rule which is stated in multiple ways in the Bible including:
He will reply, ‘Truly I tell you, whatever you did not do for one of the least of these, you did not do for me.
In a country that (says it) values meritocracy, much of the establishment doesn’t seem to put much, if any value, into these basic principles as they would like to indicate that they do.
I’ve previously highlighted the application of mathematical game theory before briefly in relation to the Golden Rule, but from a meritocracy perspective, why can’t it operate at all levels? By this I’ll make tangential reference to Cesar Hidalgo‘s thesis in his book Why Information Grows in which he looks not at just individuals (person-bytes), but larger structures like firms/companies (firmbytes), governments, and even nations. Why can’t these larger structures have their own meritocracy? When America “competes” against other countries, why shouldn’t it be doing so in a meritocracy of nations? To do this requires that we as individuals (as well as corporations, city, state, and even national governments) need to help each other out to do what we can’t do alone. One often hears the aphorism that “a chain is only as strong as it’s weakest link”, why then would we actively go out of our way to create weak links within our own society, particularly as many in government decry the cultures and actions of other nations which we view as trying to defeat us? To me the statistical mechanics of the situation require that we help each other to advance the status quo of humanity. Evolution and the Red Queeen Hypothesis dictates that humanity won’t regress back to the mean, it may be regressing itself toward extinction otherwise.
Highlights, Quotes, & Marginalia
You can often see troubles when grandparents visit a grandchild they haven’t seen for a while.
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Upon meeting her a year later, they can suffer a few awkward hours because their models are out of date.
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Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world. It is built from faulty, incomplete, or generalized data. Whether it comes from experience or hearsay, the data indicates that certain types of people have behaved badly. That generates a binary prediction that all people of that race will behave that same way.
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Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models.
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the workings of a recidivism model are tucked away in algorithms, intelligible only to a tiny elite.
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A 2013 study by the New York Civil Liberties Union found that while black and Latino males between the ages of fourteen and twenty-four made up only 4.7 percent of the city’s population, they accounted for 40.6 percent of the stop-and-frisk checks by police.
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So if early “involvement” with the police signals recidivism, poor people and racial minorities look far riskier.
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The questionnaire does avoid asking about race, which is illegal. But with the wealth of detail each prisoner provides, that single illegal question is almost superfluous.
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judge would sustain it. This is the basis of our legal system. We are judged by what we do, not by who we are.
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(And they’ll be free to create them when they start buying their own food.) I should add that my model is highly unlikely to scale. I don’t see Walmart or the US Agriculture Department or any other titan embracing my app and imposing it on hundreds of millions of people, like some of the WMDs we’ll be discussing.
You have to love the obligatory parental aphorism about making your own rules when you have your own house.
Yet the US SNAP program does just this. It could be an interesting example of this type of WMD.
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three kinds of models.
namely: baseball, food, recidivism
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The first question: Even if the participant is aware of being modeled, or what the model is used for, is the model opaque, or even invisible?
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many companies go out of their way to hide the results of their models or even their existence. One common justification is that the algorithm constitutes a “secret sauce” crucial to their business. It’s intellectual property, and it must be defended,
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the second question: Does the model work against the subject’s interest? In short, is it unfair? Does it damage or destroy lives?
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While many may benefit from it, it leads to suffering for others.
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The third question is whether a model has the capacity to grow exponentially. As a statistician would put it, can it scale?
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scale is what turns WMDs from local nuisances into tsunami forces, ones that define and delimit our lives.
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So to sum up, these are the three elements of a WMD: Opacity, Scale, and Damage. All of them will be present, to one degree or another, in the examples we’ll be covering
Think about this for a bit. Are there other potential characteristics?
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You could argue, for example, that the recidivism scores are not totally opaque, since they spit out scores that prisoners, in some cases, can see. Yet they’re brimming with mystery, since the prisoners cannot see how their answers produce their score. The scoring algorithm is hidden.
This is similar to anti-class action laws and arbitration clauses that prevent classes from realizing they’re being discriminated against in the workplace or within healthcare. On behalf of insurance companies primarily, many lawmakers work to cap awards from litigation as well as to prevent class action suits which show much larger inequities that corporations would prefer to keep quiet. Some of the recent incidences like the cases of Ellen Pao, Susan J. Fowler, or even Harvey Weinstein are helping to remedy these types of things despite individuals being pressured to stay quiet so as not to bring others to the forefront and show a broader pattern of bad actions on the part of companies or individuals. (This topic could be an extended article or even book of its own.)
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the point is not whether some people benefit. It’s that so many suffer.
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And here’s one more thing about algorithms: they can leap from one field to the next, and they often do. Research in epidemiology can hold insights for box office predictions; spam filters are being retooled to identify the AIDS virus. This is true of WMDs as well. So if mathematical models in prisons appear to succeed at their job—which really boils down to efficient management of people—they could spread into the rest of the economy along with the other WMDs, leaving us as collateral damage.
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Guide to highlight colors
Yellow–general highlights and highlights which don’t fit under another category below
Orange–Vocabulary word; interesting and/or rare word
Green–Reference to read
Red–Example to work through
I’m reading this as part of Bryan Alexander’s online book club.Syndicated copies to:
This morning, at the Friday morning coffee meetup of Innovate Pasadena held at Cross Campus, I saw one of the singularly best and most valuable talks I’ve heard in a long time. Many of these types of speakers, while engaging or even entertaining, are telling the same tired stories and at best you learn one sentence’s worth of value. Definitively not the case this morning!!!
Entitled How Women Can Succeed in the Workplace (Despite Having “Female Brains”) writer and speaker Valerie Alexander presented a brief discussion of human evolutionary history (a topic I’ve studied closely for several decades) that featured the difference in development of male and female human brains. Based on this and with a clearer picture of what broadly differentiates the sexes, Valerie then gave a multitude of highly relate-able examples from her professional life highlighting how women can simply take back control in the workplace to not only better succeed for themselves, but to also help their companies see their true value and succeed simultaneously.
Further, she also included some simple and very actionable advice (for men and women) to be able to make a better space within corporations so that they’re able to extract more of the value women bring to the workplace. Hint: Women bring a HUGE amount of value, and a majority of companies are not only undervaluing it, but they are literally throwing it away.
Not only were the messages tremendously valuable and imminently actionable by both women AND men, but she delivered it with fantastic confidence, grace, wit, charm, and warmth. In fact, I’d say it was not only strikingly informative, but it was also very entertaining. If you’re in the corporate space and looking to turn around your antediluvian or even pre-historic work culture (I’m looking ominously at you Uber and similar Silicon Valley brogrammer cultures), then jump in line as quickly as you can to book up what I can only expect is the diminishing time in her speaking and travel schedule.
Innovate Pasadena recorded the talk and I’ll try to post it here as soon as it’s available. Until then I will highly recommend purchasing her book How Women Can Succeed in the Workplace (Despite Having “Female Brains”), which I’m sure has not only the content of her lecture, but assuredly includes a whole lot more detail and additional examples than one could fit into such a short time frame. I also suspect it’s the type of book one would want to refer back to frequently as well. I’ve already got a half a dozen copies of it on their way to me to share with friends and family. I’m willing to make a substantial bet that for uncovering inherent value, this book and her overall message will eventually stand in the pantheon of texts and work of those like those of Frederick Winslow Taylor, Lillian Gilbreth, Frank Gilbreth, Dale Carnegie, Napoleon Hill, J.M. Juran, and W. Edwards Deming.
Psst… If the good folks at TED need some fantastic content, I saw a shortened 25 minute version of her hour-long talk. It could be tightened a hair for content and length, but it’s got exactly the tone, tempo and has the high level of presentation skills for which you’re known. Most importantly, it’s definitively an “Idea worth spreading.”