International Summer School on Bias and Discrimination in AI

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 suggest 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.

A basic understanding of machine learning is strongly recommended.


  • 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 will be opened in February 2019. It will be announced in our newsletter.

Preliminary Program

Day 1: Bias and inclusion in AI

  • Opening remarks
  • Keynote and Introduction
  • Understanding bias and discrimination
  • AI and social justice
  • The tech diversity problem
  • Introduction to Machine Learning and automated decision-making (optional)

Day 2: Fairness in pre-processing

  • Introduction
  • Bloc 1: Fairness in pre-processing (fairness in data)
    • Technical session
    • Case studies
    • Hands-on

Day 3: Fairness in processing and post-processing

  • Bloc 2: Fairness in processing/Fairness in design
    • Technical session
    • Case studies
  • Bloc 3: Fairness in post-processing/Fairness in decision
    • Technical session
    • Case studies

Day 4 – Governance, recommendations and future directions

  • Community-driven initiatives:
    • Montreal Declaration for a Responsible Development of Artificial Intelligence
    • Other initiatives (TBD)
  • Bias in the private sector: Challenges and recommendations
  • Bias in the public sector: Challenges and recommendations
  • Closing Panel discussion


Scientific Committee

To be announced soon.

Organizing Committee

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


For any inquiries, please contact us at