activity
20082026
most citedImproving fairness in machine learning systems: What do industry practitioners need?

758 citations · 1.3k across the 30 of their papers we have counts for

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7 papers · 1 filter

cs.GT2021

Incentive-Compatible Forecasting Competitions

Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan +2

We initiate the study of incentive-compatible forecasting competitions in which multiple forecasters make predictions about one or more events and compete for a single prize. We ha…

cs.GT2017

A Decomposition of Forecast Error in Prediction Markets

Miroslav Dudík, Sébastien Lahaie, Ryan Rogers +1

We analyze sources of error in prediction market forecasts in order to bound the difference between a security's price and the ground truth it estimates. We consider cost-function-…

cs.GT2016

The Possibilities and Limitations of Private Prediction Markets

Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan

We consider the design of private prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market pa…

cs.GT2015★ 141 cited

Incentivizing High Quality Crowdwork

Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri +1

We study the causal effects of financial incentives on the quality of crowdwork. We focus on performance-based payments (PBPs), bonus payments awarded to workers for producing high…

cs.GT2012★ 9 cited

Designing Informative Securities

Yiling Chen, Mike Ruberry, Jennifer Wortman Vaughan

We create a formal framework for the design of informative securities in prediction markets. These securities allow a market organizer to infer the likelihood of events of interest…

cs.GT2010

An Optimization-Based Framework for Automated Market-Making

Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan

Building on ideas from online convex optimization, we propose a general framework for the design of efficient securities markets over very large outcome spaces. The challenge here…