An important topic considered this year at the International Conference on Neural Information Processing Systems (NIPS), one of the prime outlets for machine learning and Artificial Intelligence research in the world, is the connection between machine learning, law and ethics. In particular, a paper presented by Moritz Hardt, Eric Price, and Nathan Srebro focused on Equality of Opportunity in Supervised Learning.
For algorithm based systems, as with many other topics, 2016 turned out to be an eventful year. As we close the year and look back on events, the course of 2016 brought many of the issues we intend to address in the UnBias project to the attention of people and organizations who previously perhaps had not considered these things before.
A lot has been said about algorithms working as gatekeepers and making decisions on our behalf, often without us noticing it. I can surely find an example in my daily life where I do notice it and benefit from it. This happens when I use the “Discover Weekly” Spotify play-list. By comparing my listening habits to that of other users with similar but not identical choices, Spotify allows information on the fringes to be shared. It is thus “tailored” to my music taste, and it is incredibly accurate in predicting things I would like. Besides, it lets me discover new music and bands and in many occasions can also take me back in time with some tunes I have probably not listened to for a long time.