A big problem with data analysis is that when it goes really deep, it’s not so easy to know why it’s working, if it’s working. Algorithms can be skewed consciously or not to favor some and keep us in separate silos, and the findings of artificial neural networks can be mysterious to even machine-learning professionals. We already base so much on silicon crunching numbers and are set to bet the foundations of our society on these operations, so that’s a huge issue. Another one: The efficacy of neural nets may be inhibited by more transparent approaches. Two pieces on the topic follow.
The opening of Aaron M. Bornstein’s Nautilus essay “Is Artificial Intelligence Permanently Inscrutable?“:
Dmitry Malioutov can’t say much about what he built.
As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM’s corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. “We couldn’t explain the model to them because they didn’t have the training in machine learning.”
In fact, it may not have helped even if they were machine learning experts. That’s because the model was an artificial neural network, a program that takes in a given type of data—in this case, the insurance company’s customer records—and finds patterns in them. These networks have been in practical use for over half a century, but lately they’ve seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.
As exciting as their performance gains have been, though, there’s a troubling fact about modern neural networks: Nobody knows quite how they work. And that means no one can predict when they might fail.•
O’Neil sees plenty of parallels between the usage of Big Data today and the predatory lending practices of the subprime crisis. In both cases, the effects are hard to track, even for insiders. Like the dark financial arts employed in the run up to the 2008 financial crisis, the Big Data algorithms that sort us into piles of “worthy” and “unworthy” are mostly opaque and unregulated, not to mention generated (and used) by large multinational firms with huge lobbying power to keep it that way. “The discriminatory and even predatory way in which algorithms are being used in everything from our school system to the criminal justice system is really a silent financial crisis,” says O’Neil.
The effects are just as pernicious. Using her deep technical understanding of modeling, she shows how the algorithms used to, say, rank teacher performance are based on exactly the sort of shallow and volatile type of data sets that informed those faulty mortgage models in the run up to 2008. Her work makes particularly disturbing points about how being on the wrong side of an algorithmic decision can snowball in incredibly destructive ways—a young black man, for example, who lives in an area targeted by crime fighting algorithms that add more police to his neighborhood because of higher violent crime rates will necessarily be more likely to be targeted for any petty violation, which adds to a digital profile that could subsequently limit his credit, his job prospects, and so on. Yet neighborhoods more likely to commit white collar crime aren’t targeted in this way.
In higher education, the use of algorithmic models that rank colleges has led to an educational arms race where schools offer more and more merit rather than need based aid to students who’ll make their numbers (thus rankings) look better. At the same time, for-profit universities can troll for data on economically or socially vulnerable would be students and find their “pain points,” as a recruiting manual for one for-profit university, Vatterott, describes it, in any number of online questionnaires or surveys they may have unwittingly filled out. The schools can then use this info to funnel ads to welfare mothers, recently divorced and out of work people, those who’ve been incarcerated or even those who’ve suffered injury or a death in the family.
Indeed, O’Neil writes that WMDs [Weapons of Math Destruction] punish the poor especially, since “they are engineered to evaluate large numbers of people. They specialize in bulk. They are cheap. That’s part of their appeal.” Whereas the poor engage more with faceless educators and employers, “the wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than a fast-food chain or a cash-strapped urban school district. The privileged… are processed more by people, the masses by machines.”•