Friday, February 23

8amRegistration Opens
8:15 - 9Light Breakfast
9 - 9:05Welcome and Awards
9:05 - 10:30Keynote 1

Speaker: Latanya Sweeney

Professor of Government and Technology in Residence at Harvard University, and Director of the Data Privacy Lab in the Institute of Quantitative Social Science at Harvard

10:30 - 11Coffee Break
11 - 12Session 1: Online Discrimination and Privacy
Potential for Discrimination in Online Targeted Advertising
Till Speicher, Muhammad Ali (MPI-SWS), Giridhari Venkatadri (Northeastern University), Filipe Nunes Ribeiro (UFOP and UFMG), George Arvanitakis (MPI-SWS), Fabrício Benevenuto (UFMG), Krishna P. Gummadi (MPI-SWS), Patrick Loiseau (Univ. Grenoble Alpes), Alan Mislove (Northeastern University)
Discrimination in Online Personalization: A Multidisciplinary Inquiry
Amit Datta, Anupam Datta (Carnegie Mellon University), Jael Makagon, Deirdre K. Mulligan (University of California, Berkeley), Michael Carl Tschantz (International Computer Science Institute)
Privacy for All: Ensuring Fair and Equitable Privacy Protections
Michael D. Ekstrand, Rezvan Joshaghani, Hoda Mehrpouyan (Boise State University)
12 - 1:30Catered Lunch
1:30 - 2:30Session 2: Interpretability and Explainability
"Meaningful Information" and the Right to Explanation
Andrew Selbst (Data & Society Research Institute), Julia Powles (Cornell Tech, NYU)
Interpretable Active Learning
Richard Phillips, Kyu Hyun Chang, Sorelle A. Friedler (Haverford College)
Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
Chelsea Barabas, Madars Virza, Karthik Dinakar, Joichi Ito (MIT), Jonathan Zittrain (Harvard)
2:30 - 3Coffee Break
3 - 4Tutorials 1

Hands On

Quantifying and Reducing Gender Stereotypes in Word Embeddings
Kai-Wei Chang (UCLA), Tolga Bolukbasi, and Venkatesh Saligrama (Boston University)

Translating to Computer Science

Understanding the Context and Consequences of Pre-trial Detention
Elizabeth Bender (Decarceration Project at The Legal Aid Society of NYC) and Kristian Lum (Human Rights Data Analysis Group)

Translating to Social Science

21 Fairness Definitions and Their Politics
Arvind Narayanan (Princeton University)

4 - 5Tutorials 2

Hands On

Auditing Black Box Models
Carlos Scheidegger (U. Arizona), Suresh Venkatasubramanian (U. Utah), and Charles Marx (Haverford College)

Translating to Computer Science

People Analytics and Employment Selection: Opportunities and Concerns
Kelly Trindel (Equal Employment Opportunity Commission / pymetrics)

Translating to Social Science

A Shared Lexicon for Research and Practice in Human-Centered Software Systems
Nitin Kohli, Renata Barreto, Joshua A. Kroll (University of California - Berkeley)

Saturday, February 24

8amRegistration Opens
8:15 - 9Light Breakfast
9 - 9:05Welcome
9:05 - 10:30Keynote 2

Speaker: Deborah Hellman

University of Virginia School of Law, D. Lurton Massee Professor of Law, Roy L. and Rosamond Woodruff Morgan Professor of Law

What is discrimination, when is it wrong and why?

We distinguish among people all the time, on the basis of all sorts of traits and for a myriad of reasons. Sometimes doing so is clearly permissible. Sometimes doing so is clearly impermissible. And sometimes people disagree about whether particular policies or practices are permissible or not. What explains which are which? There are no simple answers. Rather, philosophers and legal scholars have different ideas about which instances are wrongful discrimination and why. In addition, they disagree about what evils discrimination law aims to eradicate. In this talk, I will survey the different answers that scholars give to these questions and the debates these various approaches give rise to.

10:30 - 11Coffee Break
11 - 12Session 3: Fairness in Computer Vision and NLP
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Joy Buolamwini (MIT), Timnit Gebru (Microsoft Research)
Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies
Nishtha Madaan, Sameep Mehta (IBM Research), Taneea Agrawaal, Vrinda Malhotra, Aditi Aggarwal (IIIT- Delhi), Yatin Gupta (MSI Delhi), Mayank Saxena (DTU Delhi)
Mixed Messages? The Limits of Automated Social Media Content Analysis
Natasha Duarte, Emma Llanso (Center for Democracy & Technology), Anna Loup (University of Southern California)
12 - 1:30Catered Lunch and Poster Session
1:30 - 3Session 4: Fair Classification
The cost of fairness in binary classification
Aditya Krishna Menon (The Australian National University), Robert C Williamson (The Australian National University and Data61)
Decoupled Classifiers for Group-Fair and Efficient Machine Learning
Cynthia Dwork (Harvard), Nicole Immorlica, Adam Tauman Kalai (Microsoft Research), Mark DM Leiserson (University of Maryland)
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
Alexandra Chouldechova (Carnegie Mellon University), Diana Benavides Prado, Oleksandr Fialko, Rhema Vaithianathan (Auckland University of Technology)
Fairness in Machine Learning: Lessons from Political Philosophy
Reuben Binns (University of Oxford)
3 - 3:30Coffee Break
3:30 - 4:50Session 5: FAT Recommenders, Etc.
Runaway Feedback Loops in Predictive Policing
Danielle Ensign (University of Utah), Sorelle A. Friedler (Haverford College), Scott Neville (University of Utah), Carlos Scheidegger (University of Arizona), Suresh Venkatasubramanian (University of Utah)
All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness
Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, Maria Soledad Pera (Boise State University)
Recommendation Independence
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh (National Institute of Advanced Industrial Science and Technology (AIST)), Jun Sakuma (University of Tsukuba & RIKEN Center for Advanced Intelligence Project)
Balanced Neighborhoods for Multi-sided Fairness in Recommendation
Robin Burke, Nasim Sonboli, Aldo Ordonez-Gauger (DePaul University)
4:50 - 5Farewell