ACM FAccT - 2019 Accepted Papers

Historical Note: In 2019, the conference's name was ACM FAT*. The conference changed its name to ACM FAccT immediately following the 2020 conference.

Disclaimer: Any product or service evaluated in any of these articles, or any claim therein, is not guaranteed or endorsed by FAccT, which is not an auditing organization. All claims and opinions expressed in these articles are solely those of the authors, and do not necessarily represent those of their affiliated organizations, or those of the organizers and the reviewers of FAccT.

Explaining Explanations in AI

Brent Mittelstadt, Chris Russell, Sandra Wachter

Deep Weighted Averaging Classifiers

Dallas Card, Michael Zhang, Noah A. Smith

Fairness and Abstraction in Sociotechnical Systems

Andrew Selbst, danah boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, Janet A. Vertesi

50 Years of Test (Un)fairness: Lessons for Machine Learning

Ben Hutchinson, Margaret Mitchell

A comparative study of fairness-enhancing interventions in machine learning

Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, Derek Roth

Beyond Open vs. Closed: Balancing Individual Privacy and Public Accountability in Data Sharing

Meg Young, Luke Rodriguez, Emily Keller, Feiyang Sun, Boyang Sa, Jan Whittington, Bill Howe

Analyzing Biases in Perception of Truth in News Stories and their Implications for Fact Checking

Mahmoudreza Babaei, Abhijnan Chakraborty, Juhi Kulshrestha, Elissa M. Redmiles, Meeyoung Cha, Krishna Gummadi

Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments

Ben Green, Yiling Chen

Problem Formulation and Fairness

Samir Passi, Solon Barocas

Fairness under unawareness: assessing disparity when protected class is unobserved

Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Vivian Lai, Chenhao Tan

Actionable Recourse in Linear Classification

Berk Ustun, Alexander Spangher, Yang Liu

A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media

Stevie Chancellor, Michael Birnbaum, Eric Caine, Vincent Silenzio, Munmun De Choudhury

The Disparate Effects of Strategic Manipulation

Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan

An Algorithmic Framework to Control Polarization in Personalization

L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Nisheeth Vishnoi

Racial categories in machine learning

Sebastian Benthall, Bruce Haynes

Downstream Effects of Affirmative Action

Sampath Kannan, Aaron Roth, Juba Ziani

Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data

David Madras, Elliot Creager, Toni Pitassi, Richard Zemel

Model Reconstruction from Model Explanations

Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt

Fair Allocation through Competitive Equilibrium from Generic Incomes

Moshe Babaioff, Noam Nisan, Inbal Talgam-Cohen

An Empirical Study of Rich Subgroup Fairness for Machine Learning

Michael J. Kearns, Seth V. Neel, Aaron L. Roth, Zhiwei Steven Wu

From Soft Classifiers to Hard Decisions: How fair can we be?

Ran Canetti, Aloni Cohen, Nishanth Dikkala, Govind Ramnarayan, Sarah Scheffler, Adam Smith

Efficient Search for Diverse Coherent Explanations

Christopher Russell

Robot Eyes Wide Shut: Understanding Dishonest Anthropomorphism

Brenda Leong, Evan Selinger

A Moral Framework for Understanding Fair ML through Economic Models of Equality of Opportunity

Hoda Heidari, Michele Loi, Krishna Gummadi, Andreas Krause

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi

Access to Population-Level Signaling as a Source of Inequality

Nicole Immorlica, Katrina Ligett, Juba Ziani

Measuring the Biases that Matter: The Ethical and Causal Foundations for Measures of Fairness in Algorithms

Bruce Glymour, Jonathan Herington

Fairness-Aware Programming

Aws Albarghouthi, Samuel Vinitsky

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Jake Goldenfein

Clear Sanctions, Vague Rewards: How China’s Social Credit System Defines “Good” and “Bad” Behavior

Severin Engelmann, Mo Chen, Jens Grossklags, Felix Fischer, Ching-yu Kao

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting

Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Adam Kalai

Who's the Guinea Pig? Investigating Online A/B/n Tests In-The-Wild

Shan Jiang, John Martin, Christo Wilson

Fair Algorithms for Learning in Allocation Problems

Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Zachary Schutzman

On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook

Filipe Ribeiro, Koustuv Saha, Mahmoudreza Babaei, Lucas Henrique, Johnnatan Messias, Oana Goga, Fabricio Benevenuto, Krishna P. Gummadi, Elissa M. Redmiles

Model Cards for Model Reporting

Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Ben Hutchinson, Elena Spitzer, Lucy Vasserman, Inioluwa Deborah Raji, Timnit Gebru

Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 million people

Ziad Obermeyer, Sendhil Mullainathan

The Social Cost of Strategic Classification

Smitha Milli, John Miller, Anca Dragan, Moritz Hardt

SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments

Dimitrios Bountouridis, Jaron Harambam, Mykola Makhortykh, Monica Marrero, Nava Tintarev, Claudia Hauff

Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations

Abhijnan Chakraborty, Gourab K Patro, Niloy Ganguly, Krishna P. Gummadi, Patrick Loiseau

From Fair Decision Making To Social Equality

Hussein Mouzannar, Mesrob I. Ohannessian, Nathan Srebro, Hussein Mouzannar