Friday, February 23 Livestream

8amRegistration Opens
8:15 - 9Light Breakfast
9 - 9:20Welcome and Awards

Solon Barocas (General Chair), Sorelle Friedler, and Christo Wilson (co-Program Committee Chairs)

9:20 - 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

Saving Humanity

Technology designers are the new policymakers and AI is the new policy. No one elected these designers, and most people do not know their names, but the decisions they make when producing the latest gadgets and online innovations dictate the code by which we conduct our daily lives and govern our country. We live in a new kind of technocracy driven by AI innovation. We are moving quickly, but where are we going? As AI progresses, every demographic value and every law and historical protection comes up for grabs and will likely be redefined by what technology does or does not enable. What are the unforeseen consequences? Is humanity itself at risk and if so, how do we control our destiny? Come and let’s explore the terrain in this talk.

Discussant: Jason Schultz (NYU)

10:30 - 11Coffee Break
11 - 12Session 1: Online Discrimination and Privacy

Session Chair: Joshua Kroll (University of California, Berkeley)

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

Session Chair: Been Kim (Google Brain)

"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)
Source Code
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 - Vanderbilt Hall 204

Session Chair: Hal Daumé III (University of Maryland/Microsoft Research)

Quantifying and Reducing Gender Stereotypes in Word Embeddings

Kai-Wei Chang (UCLA), Tolga Bolukbasi, and Venkatesh Saligrama (Boston University)


Translating to Computer Science - Vanderbilt Hall 210

Session Chair: David Robinson (Upturn)

Understanding the Context and Consequences of Pre-trial Detention

Elizabeth Bender (Decarceration Project at The Legal Aid Society of NYC), Kristian Lum (Human Rights Data Analysis Group), and Terrence Wilkerson (entrepreneur)


Translating to Social Science - Vanderbilt Hall 220

Session Chair: Andrew Selbst (Data & Society Research Institute)

21 Fairness Definitions and Their Politics

Arvind Narayanan (Princeton University)


4 - 5Tutorials 2

Hands On - Vanderbilt Hall 204

Session Chair: Jennifer Wortman Vaughan (Microsoft Research)

Auditing Black Box Models

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


Translating to Computer Science - Vanderbilt Hall 210

Session Chair: Seda Gürses (KU Leuven)

People Analytics and Employment Selection: Opportunities and Concerns

Kelly Trindel (Equal Employment Opportunity Commission / pymetrics)


Translating to Social Science - Vanderbilt Hall 220

Session Chair: Karen Levy (Cornell University)

A Shared Lexicon for Research and Practice in Human-Centered Software Systems

Nitin Kohli, Renata Barreto, Joshua A. Kroll (University of California - Berkeley)


More tutorials are available here.

Saturday, February 24 Livestream

8amRegistration Opens
8:15 - 9Light Breakfast
9 - 9:20Welcome

Solon Barocas (General Chair), Sorelle Friedler, and Christo Wilson (co-Program Committee Chairs)

9:20 - 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.

Discussant: Cynthia Dwork (Harvard)

10:30 - 11Coffee Break
11 - 12Session 3: Fairness in Computer Vision and NLP

Session Chair: Hanna Wallach (Microsoft Research)

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
1:30 - 3Session 4: Fair Classification

Session Chair: Kristian Lum (Human Rights Data Analysis Group)

The cost of fairness in binary classification
Aditya Krishna Menon (The Australian National University), Robert C Williamson (The Australian National University and Data61)
Best Paper: Technical Contribution
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)
Best Paper: Technical and Interdisciplinary Contribution
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.

Session Chair: Fernando Diaz (Spotify)

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)
Source Code
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)
Source Code
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)
Source Code
Balanced Neighborhoods for Multi-sided Fairness in Recommendation
Robin Burke, Nasim Sonboli, Aldo Ordonez-Gauger (DePaul University)
4:50 - 5Farewell