Algorithmic Fairness & Opacity Lecture Series

A conversation about “humans in the loop” who invisibly deliver on-demand task-based services and the lives of the people paid to train artificial intelligence.

Eric Horvitz is a technical fellow and director at Microsoft Research.

Machine learning algorithms are not neutral observers of the world, but see what they are trained to see.

The impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America.

Issa Kohler-Hausmann studies the methodological and theoretical issues in stating and proving discrimination claims.

What to do when software algorithms are designed for statistical fairness or legal compliance, but still seem unfair.

What does it mean for algorithmic classification to be fair, and how can we promote algorithmic fairness?

Angèle Christin documents a gap between the intended and actual effects of algorithms in two professions.

Dawn Nafus is a senior research scientist at Intel Labs, where she researches cultures of quantification and health and environmental sensing.