I think in particular I would spend a lot more time on recommendation engines, although we do have a chapter on recommendation engines, from the former CEO of Hunch, I believe, to try to understand a person by 20 questions and then sort of recommend what kind of iPhone they should buy or something like that. What if I'd been teaching a different school? To me the folly of the quants is just a symptom of the larger problem of trying to optimize systems before first optimizing incentives. However, since the effectiveness of colleges is difficult to measure, the algorithm relied on various alternative and more measurable pieces of data or “proxies.” All these proxies were then combined and weighted in the algorithm to output one definitive rank. This generally does more harm than good, including to the intended beneficiaries. Right. And that's a really important part of it because when you have something that's important and secret, it's almost always going to be destructive. So could you tell me kind of the basic ingredients of what a weapon of math destruction actually is? the 2008 market crash, and for-profit college scandals. However, there exist mineable data sources that can provide insight into the more personal aspects of candidates: social networking sites. And the third example I would give is what we call recidivism risk algorithms in the criminal justice system where you have basically questionnaires that end up with a score for recidivism risk that is handed to a judge and being told to the judge this is so objective, scientific measurement of somebody's risk of recidivism, recidivism being the likelihood of being arrested after leaving prison. And then later on when somebody sues us, in discovery it's found that we knew there was a problem with this algorithm? Hugo: Agreed completely. According to National Book Foundation:[1], Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. O’Neil agrees. The entire field of economics suffers from the same delusions, as far as I’m concerned. “Nobody really understands it, it’s incredibly widespread and powerful.” We discuss the success of Nate Silver, the founder and editor-in-chief of FiveThirtyEight (a site I spent almost two years working at). So they ask questions like, "Did you grow up in a high crime neighborhood?" Cathy O’Neil’s important new book Weapons of Math Destruction, is out today, and if you’re at all interested in the social significance of how mathematics is now being used, you should go out and get a copy.She has been blogging for quite a while at Mathbabe, which you should be following, and is a good place to start if your attention span is too short for the book. Could polling be a weapon of math destruction? WMDs work like black-boxes which take poor proxies It was working with seven huge companies just in the Atlanta, Georgia area taking on the risk, which is stupid because honestly the fair hiring law, the ADA, the onus is on the large company, not on some small data vendor. Contrary to popular opinion that algorithms are purely objective, O’Neil explains in her book that “models are opinions embedded in mathematics”. And, newer methods of visualizations, which harness certain insights from data, have helped many in decision making. . But as an observation, and this goes back to the feedback loop thing, it's not just destructive for an individual, but it actually sort of undermines the original goal of the algorithm and sort of creates a destructive feedback loop on the level of society. [1] https://collegescorecard.ed.gov/search/?degree=b&major=engineering&sort=advantage:desc So Kroger's grocery store was licensing the Kronos algorithm with a license agreement that said they wouldn't understand the algorithm that Kronos had built, but they had this indemnification clause, extra contract on top of their licensing agreement that said if there's any problem with this algorithm, Kronos would pay for the problem. This book was recommended to me by a friend in data science, but I found a lot simpler than I expected. Perhaps perfect for people who haven't been reading serious features on data in our futures, and who need to catch up with something concise and easy written in layman's terms. But how does it ever learn if it was right? ( Log Out /  The government version of simply “releas[ing] loads of data on the website” so that “students can ask hero own questions about the things that matter to them,” O’Neill argues, is a “transparent, controlled by the user, and personal” alternative, “ the opposite of a WMD.” The key difference between these two systems is opacity: the former hides all the original data inputs, revealing only the single piece of data outputted by the algorithm, whilst the latter shows all the data, so that the prospective student can, in a sense, employ their own unique algorithm to decide which school is best for them. Do you have addiction problems?". For example, if bankers notice that mortgagors default in certain geographic areas, they may assume that future borrowers in those areas will also default, and banks will stop lending to those areas. Mathematicians and physicists need to develop better understanding of these two fields to contribute more in this space. So he wasn't just precluded from that one job, he was precluded from almost any minimum wage work in the area. The same goes for loan officers, admissions officers, etc. If you’re interested, the theme from one of the ‘MLConfs’ from a few months ago was on the ethics in machine learning. I hated it, but I have to admit overall it made things more fair. So that's this sort of lack of accountability is a real problem for the model. Astronomers use a lot of quantitative techniques. The policing itself spawns new data, which justifies more policing” (p87). Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. Sociologically, the algorithms are a replacement for religious precepts. She describes how the US Department of Education released data about US colleges on a website, effectively generating “individual models for each person” by allowing each person to apply their own weights and assign their own priorities (67). It's harder to quantify inequality than it is to quantify deaths. So it would look really bad for you. Despised by the rank and file of companies that I’ve worked for, Kronos software contains many aspects and automates things that previously were done by people, mostly managers. And I think of that as a data science job, even though other people might not agree. Unfortunately, her desire to reinforce her own social beliefs infuse the same kinds of bias into her analysis that she asserts exist in current WMD’s, just in the opposite direction. Wouldn’t it be interesting if these tracking technologies were smarter about the impact of crimes they were detecting, by ranking crimes in terms of their social impact at a finer granulation than an almost laughably named Part 1 and Part 2? What happens when people turn away from all college has to offer in favor of coding bootcamps and traditional certification programs? It over-simplified issues, at least for me. I think O’Niel holds back from promoting something that would really be an anti-WMD: breaking trust in higher education as a path to financial security, instilling a sense of disillusionment about what college can provide. Whereas just asking whether specific personality tests or application filter which is also used, algorithm that filters out applications for jobs, whether that is legal is a much more finite doable question, but because of the nature of those algorithms, we may send in an application for a job, we don't even know our application is being filtered by an algorithm, so how is the public going to find out it's wrong or their application was wrongly classified? Why is that okay? The effects of predictive policing, with equality before the law replaced by an algorithm that sends different degrees of law enforcement into different communities. That’s what links are for, and, in any case, anyone who wants to read Amazon reviews will see them if they follow the link to the Amazon book page included in the posting. The idea of being felt up by a stranger who already has a cynical disposition towards me makes me shudder. Is it a thing? It could have been important. However, this argument appears underdeveloped for two key reasons. It acknowledges that models aren’t going away: As a tool for identifying people in difficulty, they are amazing. Sometimes that’s good (deadbeats) sometimes that’s bad (talented employees that think different). She posits that these problematic mathematical tools share three key features: they are opaque, unregulated, and difficult to contest. N.p. So it's destructive for them. I’ll explore two of several examples found thus far. Shaw to work first in risk management and later as a data scientist at an internet media start-up. So long story short, it's basically a test to see how poor you are and how minority you are. I appreciate that O’Neil acknowledges how intrinsic these loops are to the way data can be used in these circumstances, even by those who think they are doing good. When someone is classed as “high risk”, they’re more likely to get a longer sentence and find it harder to find a job when they eventually do get out. Look, Cathy, thank you so much for coming on the show. My friend was not complaining, she thought the reforms overall a good thing, though her family had lost a lot from it. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. WEAPONS OF MATH DESTRUCTION. But I like the idea that you've moved on from a quasi-definition you had in your book Doing Data Science. Neither has anything to do with what’s actually in the book. As a result, the importance of the modelling proper and the huge freedom one has when setting up a model is massively underestimated (if perceived at all). Cathy: Well I mean, so there's lots of different answers to that.

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