FORC 2020: Accepted Papers

1. Lee Cohen, Zachary Lipton and Yishay Mansour. Efficient candidate screening under multiple tests and implications for fairness
2. Christina Ilvento. Metric Learning for Individual Fairness
3. Kevin Stangl and Avrim Blum. Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?
4. Badih Ghazi, Ravi Kumar, Pasin Manurangsi and Rasmus Pagh. Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
5. Moni Naor and Neil Vexler. Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users
6. Christopher Jung, Sampath Kannan and Neil Lutz. Service in Your Neighborhood: Fairness in Center Location
7. Aloni Cohen and Kobbi Nissim. Towards Formalizing the GDPR’s Notion of Singling Out
8. Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh and Ameya Velingker. On the Power of Multiple Anonymous Messages
9. Ashesh Rambachan and Jonathan Roth. Bias In, Bias Out? Evaluating the Folk Wisdom
10. Cynthia Dwork, Christina Ilvento and Meena Jagadeesan. Individual Fairness in Pipelines
11. Sergei Mikhalishchev and Andrei Matveenko. Attentional Role of Quota Implementation
12. Cynthia Dwork, Christina Ilvento, Guy Rothblum and Pragya Sur. Abstracting Fairness: Oracles, Metrics, and Interpretability
13. Mark Braverman and Sumegha Garg. The Role of Randomness and Noise in Strategic Classification
14. Roy Dong, Erik Miehling and Cedric Langbort. Protecting Consumers Against Personalized Pricing: A Stopping Time Approach
15. Hao Wang, Hsiang Hsu, Mario Diaz and Flavio Calmon. To Split or Not to Split: The Impact of Disparate Treatment in Classification
16. Arpita Biswas, Siddharth Barman, Amit Deshpande and Amit Sharma. Inframarginality Audit of Group-Fairness
17. Charlotte Peale, Omer Reingold and Katrina Ligett. Bounded-Leakage Differential Privacy