An important concern is that algorithms can inadvertently discriminate against minority groups and reinforce existing inequality. Typically, the worry is that when classification algorithms are trained on a dataset that itself reflects bias this may reinforce bias. However, in the world of...

We build a game-theoretic model to examine how better demand forecasting due to algorithms, machine learning and artificial intelligence affects the sustainability of collusion in an industry. We find that while better forecasting allows colluding firms to better tailor prices to demand...

We develop an economic perspective on algorithmic fairness and the surrounding empirical, theoretical and policy issues. Our perspective draws from clear parallels between algorithms and issues in economics of discrimination, crime, personnel and technological innovation; as well as more subtle...