“This program had absolutely nothing to do with race…but multi-variable equations.”
That’s what Brett Goldstein, a former policeman for the Chicago Police Department (CPD) and current Urban Science Fellow at the University of Chicago’s School for Public Policy, said about a predictive policing algorithm he deployed at the CPD in 2010. His algorithm tells police where to look for criminals based on where people have been arrested previously. It’s a “heat map” of Chicago, and the CPD claims it helps them allocate resources more effectively.
Chicago police also recently collaborated with Miles Wernick, a professor of electrical engineering at Illinois Institute of Technology, to algorithmically generate a “heat list” of 400 individuals it claims have thehighest chance of committing a violent crime. In response to criticism, Wernick said the algorithm does not use “any racial, neighborhood, or other such information” and that the approach is “unbiased” and “quantitative.” By deferring decisions to poorly understood algorithms, industry professionals effectively shed accountability for any negative effects of their code.
But do these algorithms discriminate, treating low-income and black neighborhoods and their inhabitants unfairly? It’s the kind of question many researchers are starting to ask as more and more industries use algorithms to make decisions. It’s true that an algorithm itself is quantitative – it boils down to a sequence of arithmetic steps for solving a problem. The danger is that these algorithms, which are trained on data produced by people, may reflect the biases in that data, perpetuating structural racism and negative biases about minority groups.
There are a lot of challenges to figuring out whether an algorithm embodies bias. First and foremost, many practitioners and “computer experts” still don’t publicly admit that algorithms can easily discriminate.More and more evidence supports that not only is this possible, but it’s happening already. The law is unclear on the legality of biased algorithms, and even algorithms researchers don’t precisely understand what it means for an algorithm to discriminate….
While researchers clearly understand the theoretical dangers of algorithmic discrimination, it’s difficult to cleanly measure the scope of the issue in practice. No company or public institution is willing to publicize its data and algorithms for fear of being labeled racist or sexist, or maybe worse, having a great algorithm stolen by a competitor.
Even when the Chicago Police Department was hit with a Freedom of Information Act request, they did not release their algorithms or heat list, claiming a credible threat to police officers and the people on the list. This makes it difficult for researchers to identify problems and potentially provide solutions.
Existing discrimination law in the United States isn’t helping. At best, it’s unclear on how it applies to algorithms; at worst, it’s a mess. Solon Barocas, a postdoc at Princeton, and Andrew Selbst, a law clerk for the Third Circuit US Court of Appeals, argued together that US hiring law fails to address claims about discriminatory algorithms in hiring.
The crux of the argument is called the “business necessity” defense, in which the employer argues that a practice that has a discriminatory effect is justified by being directly related to job performance….(More)”