output
20092026
most citedImproving fairness in machine learning systems: What do industry practitioners need?

758 citations

Showing 2020Show all

6 papers · 1 filter

cs.SI2020★ 172 cited

Examining the consumption of radical content on YouTube

Homa Hosseinmardi, Amir Ghasemian, Aaron Clauset +3

Although it is under-studied relative to other social media platforms, YouTube is arguably the largest and most engaging online media consumption platform in the world. Recently, Y…

cs.AI2020★ 19 cited

Learning Models of Individual Behavior in Chess

Reid McIlroy-Young, Russell Wang, Siddhartha Sen +2

AI systems that can capture human-like behavior are becoming increasingly useful in situations where humans may want to learn from these systems, collaborate with them, or engage w…

cs.AI2020★ 80 cited

Aligning Superhuman AI with Human Behavior: Chess as a Model System

Reid McIlroy-Young, Siddhartha Sen, Jon Kleinberg +1

As artificial intelligence becomes increasingly intelligent---in some cases, achieving superhuman performance---there is growing potential for humans to learn from and collaborate…

cs.CY2020

Mathematical Foundations for Social Computing

Yiling Chen, Arpita Ghosh, Michael Kearns +2

Social computing encompasses the mechanisms through which people interact with computational systems: crowdsourcing systems, ranking and recommendation systems, online prediction m…

cs.LG2020★ 3 cited

Bandits with adversarial scaling

Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme

We study "adversarial scaling", a multi-armed bandit model where rewards have a stochastic and an adversarial component. Our model captures display advertising where the "click-thr…

cs.GT2020★ 12 cited

Equitable Allocations of Indivisible Chores

Rupert Freeman, Sujoy Sikdar, Rohit Vaish +1

We study fair allocation of indivisible chores (i.e., items with non-positive value) among agents with additive valuations. An allocation is deemed fair if it is (approximately) eq…