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FAT 2018: New York, NY, USA
- Sorelle A. Friedler, Christo Wilson:
Conference on Fairness, Accountability and Transparency, FAT 2018, 23-24 February 2018, New York, NY, USA. Proceedings of Machine Learning Research 81, PMLR 2018
Preface
- Preface. 1-2
Keynotes
- Latanya Sweeney:
Keynote 1. 3 - Deborah Hellman:
Keynote 2. 4
Contributed Papers
- Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Nunes Ribeiro, George Arvanitakis, Fabrício Benevenuto, Krishna P. Gummadi
, Patrick Loiseau, Alan Mislove:
Potential for Discrimination in Online Targeted Advertising. 5-19 - Amit Datta, Anupam Datta, Jael Makagon, Deirdre K. Mulligan, Michael Carl Tschantz:
Discrimination in Online Personalization: A Multidisciplinary Inquiry. 20-34 - Michael D. Ekstrand
Privacy for All: Ensuring Fair and Equitable Privacy Protections. 35-47 - Andrew Selbst, Julia Powles:
"Meaningful Information" and the Right to Explanation. 48 - Richard L. Phillips, Kyu Hyun Chang, Sorelle A. Friedler:
Interpretable Active Learning. 49-61 - Chelsea Barabas, Madars Virza, Karthik Dinakar, Joichi Ito, Jonathan Zittrain:
Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment. 62-76 - Joy Buolamwini, Timnit Gebru:
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. 77-91 - Nishtha Madaan, Sameep Mehta, Taneea S. Agrawaal, Vrinda Malhotra, Aditi Aggarwal, Yatin Gupta, Mayank Saxena:
Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies. 92-105 - Natasha Duarte, Emma Llansó, Anna C. Loup:
Mixed Messages? The Limits of Automated Social Media Content Analysis. 106 - Aditya Krishna Menon, Robert C. Williamson:
The cost of fairness in binary classification. 107-118 - Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Mark D. M. Leiserson:
Decoupled Classifiers for Group-Fair and Efficient Machine Learning. 119-133 - Alexandra Chouldechova, Diana Benavides Prado
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. 134-148 - Reuben Binns:
Fairness in Machine Learning: Lessons from Political Philosophy. 149-159 - Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, Suresh Venkatasubramanian:
Runaway Feedback Loops in Predictive Policing. 160-171 - Michael D. Ekstrand
All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. 172-186 - Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma:
Recommendation Independence. 187-201 - Robin Burke, Nasim Sonboli, Aldo Ordonez-Gauger:
Balanced Neighborhoods for Multi-sided Fairness in Recommendation. 202-214
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