International Summer School on Bias and Discrimination in AI

JUNE 3-6, 2019 | MONTRÉAL

About

Algorithms, and the data they process, play an increasingly important role in decisions with significant consequences for human welfare. While algorithmic decision-making processes have the potential to lead to fairer and more objective decisions, emerging research suggests that they can also lead to unequal and unfair treatments and outcomes for certain groups or individuals.

This Summer School is an attempt to engage multi-disciplinary teams of researchers and practitioners to explore the social and technical dimensions of bias, discrimination and fairness in machine learning and algorithm design. The course focuses specifically (although not exclusively) on gender, race and socioeconomic based bias and data-driven predictive models leading to decisions.

The summer school will be filmed and will be available as a MOOC late 2019.

A basic understanding of machine learning is strongly recommended.

What you will learn

  • Understanding bias and discrimination
  • Exploring the harms from bias in machine learning (discriminatory effects of algorithmic decision-making)
  • Identifying the sources of bias and discrimination in machine learning
  • Mitigating bias in machine learning (strategies for addressing bias)
  • Recommendations to guide the ethical development and evaluation of algorithms

Registration

Registration will open on March 11th at 12h00.

  • Government and industry employees: $1500
  • Non-profit organization (NPO): $400
  • Student: $200

Organizing Committee

  • Jihane Lamouri, Diversity coordinator, IVADO
  • Golnoosh Farnadi, Postdoc researcher, IVADO
  • Martin Gibert, Ethics researcher, IVADO
  • Brian Moore, Training coordinator, IVADO

Contact

For any inquiries, please contact us at formations@ivado.ca.

Confirmed Speakers

Behrouz Babaki

IVADO Postdoctoral Researcher at Polytechnique Montreal

Fernando Diaz

Principal Researcher and lead of the Montreal FATE Research Group at Microsoft

Marc-Antoine Dilhac

Professor of Philosophy at the Université de Montréal, Chair Holder…

Golnoosh Farnadi

IVADO Postdoctoral Researcher at Polytechnique Montreal

Abhishek Gupta

Founder, Montreal AI Ethics Institute

Moritz Hardt

Assistant Professor, University of California, Berkeley

Yasmeen Hitti

Research Intern at Mila and co-founder of Biasly AI

Andrea Jang

Research Intern at Mila and co-founder of Biasly AI

Emre Kiciman

Principal Researcher at Microsoft Research AI

François Laviolette

Professor at Université Laval and Director of the Big Data Research Center

Margaret Mitchell

Senior Research Scientist, Google Research and Machine Intelligence

Petra Molnar

Lawyer and Researcher at the International Human Rights Program…

Deborah Raji

Engineering Science Student, University of Toronto

Tania Saba

Professor of Industrial Relations at Université de Montréal, Chair Holder…

Pedro Saleiro

Post-Doc, Aequitas, Center for Data Science and Public Policy at the University of Chicago

Cynthia Savard Saucier

Director UX at Shopify and co-author of Tragic Design

Luke Stark

Postdoctoral Researcher at Microsoft Research Montreal

Rachel Thomas

Professor at the University of San Francisco and co-founder of fast.ai

Nicolas Vermeys

Professor of Law at Université de Montréal and Assistant Director of the Cyberjustice Laboratory

RC Woodmass

Founder of Queerit and Product Designer at Crescendo

Preliminary Program

Day 1: Bias and inclusion in AI

Moderator

To be confirmed

The tech diversity problem

Understanding bias and discrimination

Synthesis

To be confirmed

Day 2: Mitigating Bias

Bias and fairness in AI for public policy

The Aequitas toolkit: case studies & tutorial

Where does data bias come from?

Fairness definitions and their policies

Bias in machine learning algorithm: how algorithms can amplify, prevent or mitigate bias

Social event/cocktail

Day 3: Mitigating Bias

Fairness-aware machine learning: practical challenges

Learning subject to fairness constraints

Tackling gender bias in text (Biasly AI)

Day 4 – Governance, recommendations and future directions

Moderator

To be confirmed

Keynote

Terrence Wilkerson, Entrepreneur

Automated decision system technologies and human rights

The Montreal Declaration for a Responsible Development of AI

The impact of bad design and how to fix it

Building inclusive teams

Bias in the private sector, recommendations for the future

Fernando Diaz & Luke Stark, Microsoft FATE