We’ve become suspicious of tech. Once obscure issues like the design of news recommendation systems and the evaluation of criminal justice risk scoring methods have become major public conversations. This new scrutiny of technical systems with social impact is well deserved — but that doesn’t mean that we get to be sloppy when investigating the power of algorithms.
The journalistic investigation of the politics of code, sometimes called algorithmic accountability reporting, is one of the more complicated types of reporting to do. The interactions between the technical and the social are intricate. Covering California’s SB 10 bill, which mandates the use of pre-trial statistical risk assessment, requires an understanding of both machine learning error rates and the contentious politics of bail reform. The New York Times tried to make the case that increased social media use in Germany is correlated with more violent attacks on refugees, but the “landmark” study they relied on was actually a preliminary paper. Subsequent analysis suggests that the truth might end up turning on the number of people in the Facebook Nutella group, which the researchers used as a proxy for social media use generally. This stuff is tricky — and because the stakes are so high, everyone has strong opinions.
I’ve already seen several cringe-worthy examples of simplistic, unfounded, or just plain biased reporting on algorithms, including misleading pieces from both tech cheerleaders and tech skeptics. In the hopes of seeing better work in the future, here are a few tips on getting an algorithm story right.
Like any serious journalism, algorithmic accountability reporting requires expertise, curiosity, and dedication to the truth. Increased skepticism of our robot overlords is a good thing, but it doesn’t get to play by different standards than any other investigative journalism.
Jonathan Stray is a computational journalist teaching and researching at Columbia University’s School of Journalism.
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