As firms are moving towards data-driven decision making, they are facing an emerging
problem, namely, algorithmic bias. Accordingly, algorithmic systems can yield socially …

Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates
systemic discrimination in automated decision‐making, providing opportunities to improve …

The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

" We used to think that if we improved policing we could escape its violence," Rachel
Herzing began. 1 It was November 2014, the fall that Darren Wilson killed Michael Brown …

Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic
risk assessment to estimate the likelihood that a person will commit future crime. As many …

The rapid developments in Artificial Intelligence (AI) and the intensification in the adoption of
AI in domains such as autonomous vehicles, lethal weapon systems, robotics and alike pose …

Organizations are increasingly deploying technologies that have the ability to parse through
large amounts of data, acquire skills and knowledge, and operate autonomously. These …

Big data and Artificial Intelligence (" A") are revolutionizing the ways in which firms,
governments, and employers classify individuals. Surprisingly, however, one of the most …

Algorithmic decision making is both increasingly common and increasingly controversial.
Critics worry that algorithmic tools are not transparent, accountable, or fair. Assessing the …