This is based on 3 projects, 2 of which have working papers:

Fairness, equality, and power in algorithmic decision making (joint with Rediet Abebe)
Accepted for publication, ACM Conference on Fairness, Accountability, and Transparency, 2021
Manuscript: fairness_equality_power.pdf

Adaptive Combinatorial Allocation (joint with Alex Teytelboym)
Working paper, 2020
Manuscript: adaptive_combinatorial.pdf

An active public debate is taking place about the promises and perils of algorithmic decision making, artificial intelligence, and machine learning in socially relevant contexts, such as hiring, consumer credit, bail setting, news feed selection, pricing, etc.:
Are algorithms discriminating? Can algorithmic decisions be explained? Does AI create unemployment? Can we protect individual privacy?
Some of these debates have been taken up in computer science, under headers such as privacy, fairness, accountability, and transparency.
These debates raise important conceptual questions: What are the normative foundations for these concerns? And how can we evaluate decision making systems empirically?
Economists (among others) have debated related questions in non-automated settings for a long time.
In this talk I will discuss several projects which contribute economic perspectives on the social impact of algorithmic decision making.