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...

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...

Data protection and privacy rights afforded to consumers by General Data Protection Regulation (GDPR) have presented a challenge to the firms that depend on private consumer data for targeted pricing, product recommendations and other purposes. Specifically, GDPR affords consumers the right to...

Automated pricing comes in two forms - rule-based (e.g., targeting or undercutting the lowest price, etc) and artificial intelligence (AI) powered algorithms (e.g., reinforcement learning (RL) based). While rule-based pricing is the most widely used automated pricing strategy today, many...

Machine learning (ML) algorithms used by financial lenders in their screening processes are hidden from the consumers who are affected by their decisions leading many consumers to make sub-optimal decisions when seeking credit. Despite increasing calls for greater transparency, only a few...