The societal significance of fair machine learning (ML) cannot be overstated, yet quantifying algorithmic bias and ensuring fair ML remains a challenging task. One popular fair ML objective, equality of opportunity, requires equal treatment for individuals who are equally deserving, regardless...

We investigate the long-term impact of competing against superstars in crowdsourcing contests. Using a unique 50-month longitudinal panel data set on 1677 software design crowdsourcing contests, we illustrate a learning effect where participants are able to improve their skills (learn) more when...

Should firms that apply machine learning algorithms in their decision making make their algorithms transparent? Despite increasing calls for algorithmic transparency, most firms have kept their algorithms opaque citing potential gaming by users that may negatively affect the algorithm's...

This study examines whether developers learn from their experience and from interactions with peers in OSS projects. A Hidden Markov Model (HMM) is proposed that allows us to investigate (1) the extent to which OSS developers actually learn from their own experience and from interactions with...