FAT/ML 2018 is co-located with ICML 2018. All sessions will take place on Sunday July 15th in Room A3 of the Stockholmsmässan Exhibit and Convention Center in Stockholm (map). A list of transportation options is available on the venue website.
On Formalizing Fairness in Prediction with ML Pratik Gajane and Mykola Pechenizkiy
"Fair" Risk Assessments: A Precarious Approach for Criminal Justice Reform Ben Green
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity Hee Jung Ryu, Margaret Mitchell and Hartwig Adam
Darling or Babygirl? Investigating Stylistic Bias in Sentiment Analysis Judy Hanwen Shen, Lauren Fratamico, Iyad Rahwan and Alexander M. Rush
Achieving Fairness through Adversarial Learning: an Application to Recidivism PredictionChristina Wadsworth, Francesca Vera and Chris Piech
Actionable Recourse in Linear Classification Alexander Spangler and Berk Ustun
Axiomatic Characterization of Data-Driven Influence Measures for Classification Jakub Sliwinski, Martin Strobel and Yair Zick
Datasheets for Datasets Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III and Kate Crawford
Debiasing Representations by Removing Unwanted Variation Due to Protected Attributes Amanda Bower, Laura Niss, Yuekai Sun and Alexander Vargo
Does Removing Stereotype Priming Remove Bias? A Pilot Human-Robot Interaction Study Tobi Ogunyale, De'Aira Bryant and Ayanna Howard
Enhancing Human Decision Making via Assignment Optimization Isabel Valera, Adish Singla and Manuel Gomez-Rodriguez
Equal Protection Under the Algorithm: A Legal-Inspired Framework for Identifying Discrimination in Machine Learning Sucheta Soundarajan and Daniel Clausen
Fairness Through Computationally-Bounded AwarenessMichael P. Kim, Omer Reingold and Guy Rothblum
Game-theoretic Interpretability for Temporal ModelingGuang-He Lee, David Alvarez-Melis and Tommi Jaakkola
Gradient Reversal Against Discrimination Edward Raff and Jared Sylvester
Group Fairness Under Composition Christina Ilvento and Cynthia Dwork
Individual Fairness Under Composition Christina Ilvento and Cynthia Dwork
Modelling Mistrust in End-of-Life Care Willie Boag, Harini Suresh, Leo Celi, Peter Szolovits and Marzyeh Ghassemi
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness Seth Neel, Michael Kearns, Aaron Roth and Zhiwei Steven Wu
Probably Approximately Metric-Fair Learning Gal Yona and Guy Rothblum
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics Roel Dobbe, Sarah Dean, Thomas Gilbert and Nitin Kohli
Training Fairness-Constrained Classifiers To Generalize Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth and Seungil You
Using image fairness representations in diversity-based re-ranking for recommendations Chen Karako and Putra Manggala
Women also Snowboard: Overcoming Bias in Captioning Models Lisa Anne Hendricks, Anna Rohrbach, Kaylee Burns, Trevor Darrell and Kate Saenko
Welfare and Distributional Impacts of Fair Classification Lily Hu and Yiling Chen
Blind Justice: Fairness with Encrypted Sensitive Attributes Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi and Adrian Weller
Learning under selective labels in the presence of expert consistency Maria De-Arteaga, Artur Dubrawski and Alexandra Chouldechova
Turing Box: An Experimental Platform for the Evaluation of AI Systems Ziv Epstein, Blakeley H. Payne, Judy Hanwen Shen, Casey Jisoo Hong, Bjarke Felbo, Abhimanyu Dubey, Matthew Groh, Nick Obradovich, Manuel Cebrian and Iyad Rahwan