Machine learning models are becoming increasingly opaque to human examination, even to their designers.  Yet these models are also increasingly used to make high-stakes decisions; who goes to jail, what neighborhoods police deploy to, and who should be hired for a job.  But how can we practically achieve accountability and transparency in the face of increasingly complex models?  And how do we know if the algorithmic decisions are fair or discriminatory - what does it mean for an algorithm to be fair?  In this talk, we’ll discuss recent work from the new and growing field of Fairness, Accou