Please refer to the poster ID and category to navigate gather.town during the poster session
Full Schedule with links to zoom: https://neurips.cc/virtual/2021/workshop/21850
Information Booklet : https://drive.google.com/file/d/1Bbq2DAnScbUufcwvxwkqCtF1lvc5TD1s/view
Oral Presentations
P2: Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curatio
Agnieszka Słowik · Leon Bottou
P31: On the Impossibility of Fairness-Aware Learning from Corrupted Data
Nikola Konstantinov · Christoph Lampert
P33: Achieving Counterfactual Fairness for Causal Bandit
Wen Huang · Lu Zhang · Xintao Wu
P15: The Many Roles that Causal Reasoning Plays in Reasoning about Fairness in Machine Learning
Irene Y Chen · Hal Daumé III · Solon Barocas
P3: Detecting Bias in the Presence of Spatial Autocorrelation
Subhabrata Majumdar · Cheryl Flynn · Ritwik Mitra
P7: Fair Clustering Using Antidote Data
Anshuman Chhabra · Adish Singla · Prasant Mohapatra
Posters
P6: Fairness for Robust Learning to Rank
Omid Memarrast · Ashkan Rezaei · Rizal Fathony · Brian Ziebart
P10: Cooperative Multi-Agent Fairness and Equivariant Policies
Niko Grupen · Bart Selman · Daniel Lee
P12: Fair SA: Sensitivity Analysis for Fairness in Face Recognition
Aparna Joshi · Xavier Suau Cuadros · Nivedha Sivakumar · Luca Zappella · Nicholas Apostoloff
P19: Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Networks
Ziliang Zong · Cody Blakeney · Gentry Atkinson · Nathaniel Huish · yyan34 · Vangelis Metsis
P20: Bounded Fairness Transferability subject to Distribution Shift
Reilly Raab · Yatong Chen · Yang Liu
P24: Counterfactual Fairness in Mortgage Lending via Matching and Randomization
Sama Ghoba · Nathan Colaner
P25: Structural Interventions on Automated Decision Making Systems
efren cruz · Sarah Rajtmajer Rajtmajer · Debashis Ghosh
P27: Balancing Robustness and Fairness via Partial Invariance
Moulik Choraria · Ibtihal Ferwana · Ankur Mani · Lav Varshney
P33: Implications of Modeled Beliefs for Algorithmic Fairness in Machine Learning
Ruth Urner · Jeff Edmonds · Karan Singh
P29: Fairness Degrading Adversarial Attacks Against Clustering Algorithms
Anshuman Chhabra · Adish Singla · Prasant Mohapatra
Extended Abstracts
P22: Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings
Alan Mishler · Niccolo Dalmasso
