A Guide to Solving Social Problems with Machine Learning

You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like? You're also not reassured by the vendors the city interacts with.