LIPIcs, Volume 156, FORC 2020

• Aaron Roth LIPIcs, Volume 156, FORC 2020, Complete Volume 10.4230/LIPIcs.FORC.2020
• Aaron Roth Front Matter, Table of Contents, Preface, Conference Organization 10.4230/LIPIcs.FORC.2020.0
• Lee Cohen, Zachary C. Lipton, and Yishay Mansour Efficient Candidate Screening Under Multiple Tests and Implications for Fairness 10.4230/LIPIcs.FORC.2020.1
• Christina Ilvento Metric Learning for Individual Fairness 10.4230/LIPIcs.FORC.2020.2
• Avrim Blum and Kevin Stangl Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? 10.4230/LIPIcs.FORC.2020.3
• Moni Naor and Neil Vexler Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users 10.4230/LIPIcs.FORC.2020.4
• Christopher Jung, Sampath Kannan, and Neil Lutz Service in Your Neighborhood: Fairness in Center Location 10.4230/LIPIcs.FORC.2020.5
• Ashesh Rambachan and Jonathan Roth Bias In, Bias Out? Evaluating the Folk Wisdom 10.4230/LIPIcs.FORC.2020.6
• Cynthia Dwork, Christina Ilvento, and Meena Jagadeesan Individual Fairness in Pipelines 10.4230/LIPIcs.FORC.2020.7
• Cynthia Dwork, Christina Ilvento, Guy N. Rothblum, and Pragya Sur Abstracting Fairness: Oracles, Metrics, and Interpretability 10.4230/LIPIcs.FORC.2020.8
• Mark Braverman and Sumegha Garg The Role of Randomness and Noise in Strategic Classification 10.4230/LIPIcs.FORC.2020.9
• Katrina Ligett, Charlotte Peale, and Omer Reingold Bounded-Leakage Differential Privacy 10.4230/LIPIcs.FORC.2020.10