NEW YORK CITY - 30 January 2018
The 2018 Conference on Fairness, Accountability, and Transparency (FAT*) is a first-of-its-kind international and interdisciplinary peer-reviewed conference that seeks to publish and present work examining the fairness, accountability, and transparency of algorithmic systems. FAT* will host the presentation of research work from a wide variety of disciplines, including computer science, statistics, the social sciences, and law. It takes place on February 23 and 24, 2018, at the New York University Law School, in cooperation with its Technology Law and Policy Clinic. The conference will bring together over 450 attendees (including academic researchers, policymakers, and practitioners) and will be recorded and live streamed for those who cannot attend.
FAT* builds on the success of the Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), which has been running for over four years, as well as workshops on recommender systems (FAT/REC), natural language processing (Ethics in NLP), and data and algorithmic transparency (DAT), among others. The conference will bring together this burgeoning technical research community with scholars from the social sciences, public policy, law, and other fields to create a truly interdisciplinary exchange of ideas on responsible algorithmic systems. “In a matter of years, we’ve been able to establish a vibrant and diverse community uniquely committed to practical responses to the legal, policy, and ethical challenges posed by recent advances in computing,” says General Chair Solon Barocas, an assistant professor at Cornell University.
This year's program, selected by a scientific committee of 46 top researchers across many disciplines, includes 17 peer-reviewed papers and 6 tutorials from leading experts in the field (including scientists, lawyers, and policymakers). Further, the conference will feature keynote addresses from Latanya Sweeney, Professor of Government and Technology in Residence at Harvard University, and Director of the Data Privacy Lab in the Institute of Quantitative Social Science at Harvard University, and Deborah Hellman, D. Lurton Massee Professor of Law, Roy L. and Rosamond Woodruff Morgan Professor of Law at the University of Virginia School of Law.
The program committee also chose to recognize two papers with awards. The first Best Paper award, chosen for its strong and focused technical contribution, goes to Aditya Krishna Menon and Robert C. Williamson for their paper The Cost of Fairness in Binary Classification. This paper studies the trade-off between fairness and accuracy in binary classification, quantifying the trade-off and providing an algorithm to achieve it. The Best Paper award for a strong technical contribution with an interdisciplinary lens was given to Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan for their contribution A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. This paper gives a careful case study and fairness-focused analysis of the application of risk prediction to child protective services deployed in Allegheny County, PA, USA. Program Committee co-chair Sorelle Friedler, an assistant professor at Haverford College, points out that “this combined analysis, including both statistical assessment and social context, is an example of the type of work we hope FAT* continues to encourage.”
Funded by grants from the Ethics and Governance of AI Initiative, The MacArthur Foundation, Google, Facebook Research, and H20.ai, the conference will bring together academic researchers, policymakers, and practitioners for an overview of the state of the art in designing, building, and operating fair, accountable, transparent, and responsible algorithmic systems.
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Joshua Kroll () & Suresh Venkatsubramanian ()
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