2016 Schedule

08:30 Breakfast
09:00 Introduction

Solon Barocas
09:15 Opening Panel: Setting the Stage

Rayid Ghani, Sorelle Friedler, Cynthia Rudin, and danah boyd (moderator)
10:00 Spotlight Session
- Equality of Opportunity in Supervised Learning – Eric Price, Nati Srebro, and Moritz Hardt
- Fairness in Learning: Classic and Contextual Bandits – Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth
- To Predict and Serve? – Kristian Lum and William Isaac
- Combatting Police Discrimination in The Age of Big Data – Sharad Goel, Maya Perelman, Ravi Shroff, and David Alan Sklansky
- How the Machine ‘Thinks:’ Understanding Opacity in Machine Learning Algorithms – Jenna Burrell
10:30 Morning Break
11:00 Morning Session
- Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings – Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai
- Semantics Derived Automatically from Language Corpora Necessarily Contain Human Biases – Aylin Caliskan-Islam, Joanna J. Bryson, and Arvind Narayanan
- How to be Fair and Diverse? – L. Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth Vishnoi
- Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI – Sarah Bird, Solon Barocas, Fernando Diaz, Hanna Wallach, and Kate Crawford
- Rawlsian Fairness for Machine Learning – Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth
13:00 Enforcing Human Rights Laws in an Era of Algorithms

Carmelyn Malalis
13:10 Lunch
With poster session from 1:30pm, including:
- Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models – Julius Adebayo and Lalana Kagal
- Price of Transparency in Strategic Machine Learning – Emrah Akyol, Cedric Langbort, and Tamer Basar
- Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments – Alexandra Chouldechova
- The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems – Miguel Ferreira, Muhammad Bilal Zafar, and Krishna Gummadi
- Fair Learning in Markovian Environments – Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth
- Inherent Trade-Offs in the Fair Determination of Risk Scores – Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan
- A Statistical Framework for Fair Predictive Algorithms – Kristian Lum and James Johndrow
- Measuring Fairness in Ranked Outputs – Ke Yang and Julia Stoyanovich
- Fairness Beyond Disparate Treatment and Disparate Impact: Learning Classification without Disparate Mistreatment – Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna Gummadi
14:40 Afternoon Session
- Interpretable Classification Models for Recidivism Prediction – Jiaming Zeng, Berk Ustun, and Cynthia Rudin
- Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems – Anupam Datta, Shayak Sen, and Yair Zick
- ‘Why Should I Trust You?’ Explaining the Predictions of Any Classifier –Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin
- Fairness as a Program Property – Aws Albarghouthi, Loris D'Antoni, Samuel Drews, and Aditya Nori
16:30 Afternoon Break
17:00 Closing panel: Building a community and setting a research agenda

Bettina Berendt, Kate Crawford, Jon Kleinberg, Hanna Wallach, and Suresh Venkatasubramanian (moderator)

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