Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds | Nathan Kallus · Angela Zhou |
Assessing Social and Intersectional Biases in Contextualized Word Representations | Yi Chern Tan · L. Elisa Celis |
Balancing Efficiency and Fairness in On-Demand Ridesourcing | Nixie S Lesmana · Xuan Zhang · Xiaohui Bei |
Characterizing Bias in Classifiers using Generative Models | Daniel McDuff · Shuang Ma · Yale Song · Ashish Kapoor |
Demystifying Black-box Models with Symbolic Metamodels | Ahmed Alaa · Mihaela van der Schaar |
Envy-Free Classification | Maria-Florina Balcan · Travis Dick · Ritesh Noothigattu · Ariel Procaccia |
Fair Algorithms for Clustering | Suman Bera · Deeparnab Chakrabarty · Nicolas Flores · Maryam Negahbani |
Modeling Conceptual Understanding in Image Reference Games | Rodolfo Corona Rodriguez · Stephan Alaniz · Zeynep Akata |
Multi-Criteria Dimensionality Reduction with Applications to Fairness | Uthaipon Tantipongpipat · Samira Samadi · Mohit Singh · Jamie Morgenstern · Santosh Vempala |
Noise-tolerant fair classification | Alex Lamy · Ziyuan Zhong · Aditya Menon · Nakul Verma |
On the Accuracy of Influence Functions for Measuring Group Effects | Pang Wei Koh · Kai-Siang Ang · Hubert Teo · Percy Liang |
Paradoxes in Fair Machine Learning | Paul Goelz · Anson Kahng · Ariel Procaccia |
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness | Yongkai Wu · Lu Zhang · Xintao Wu · Hanghang Tong |
This Looks Like That: Deep Learning for Interpretable Image Recognition | Chaofan Chen · Oscar Li · Daniel Tao · Alina Barnett · Cynthia Rudin · Jonathan K Su |
Towards Automatic Concept-based Explanations | Amirata Ghorbani · James Wexler · James Zou · Been Kim |
Ask not what AI can do, but what AI should do: Towards a framework of task delegability | Brian Lubars · Chenhao Tan |
Attribution-Based Confidence Metric For Deep Neural Networks | Susmit Jha · Sunny Raj · Steven Fernandes · Sumit K Jha · Somesh Jha · Brian Jalaian · Gunjan Verma · Ananthram Swami |
Average Individual Fairness: Algorithms, Generalization and Experiments | Saeed Sharifi-Malvajerdi · Michael Kearns · Aaron Roth |
Disentangling Influence: Using disentangled representations to audit model predictions | Charles Marx · Richard Phillips · Sorelle Friedler · Carlos Scheidegger · Suresh Venkatasubramanian |
Equal Opportunity in Online Classification with Partial Feedback | Yahav Bechavod · Katrina Ligett · Aaron Roth · Bo Waggoner · Steven Wu |
Exploring Algorithmic Fairness in Robust Graph Covering Problems | Aida Rahmattalabi · Phebe Vayanos · Anthony Fulginiti · Eric Rice · Bryan Wilder · Amulya Yadav · Milind Tambe |
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness | Xueru Zhang · Mohammadmahdi Khaliligarekani · Cem Tekin · mingyan liu |
Inherent Tradeoffs in Learning Fair Representations | Han Zhao · Geoff Gordon |
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification | Evgenii Chzhen · Christophe Denis · Mohamed Hebiri · Luca Oneto · Massimiliano Pontil |
Offline Contextual Bandits with High Probability Fairness Guarantees | Blossom Metevier · Stephen Giguere · Sarah Brockman · Ari Kobren · Yuriy Brun · Emma Brunskill · Philip Thomas |
On Relating Explanations and Adversarial Examples | Alexey Ignatiev · Nina Narodytska · Joao Marques-Silva |
On Testing for Biases in Peer Review | Ivan Stelmakh · Nihar Shah · Aarti Singh |
On the (In)fidelity and Sensitivity of Explanations | Chih-Kuan Yeh · Cheng-Yu Hsieh · Arun Suggala · David Inouye · Pradeep Ravikumar |
Policy Learning for Fairness in Ranking | Ashudeep Singh · Thorsten Joachims |
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric | Nathan Kallus · Angela Zhou |
Unlocking Fairness: a Trade-off Revisited | Michael Wick · swetasudha panda · Jean-Baptiste Tristan |