Study the ethical and societal dimensions and implications of AI.

Vision

To explore and anticipate the ethical and societal challenges of Artificial Intelligence (AI) based on the principles of co-construction, transversality and scientific monitoring. Regroupement 5 (R5) aims to shape AI that respects human values and is attentive to the needs of marginalized communities.

Objectives

  • Produce cutting-edge research on subjects at the heart of the ethical and societal issues linked to the development and implementation of AI.
  • Set up a scientific monitoring system to identify new issues relating to the ethical and societal challenges of AI.
  • Forge viable and sustainable co-construction relationships with representatives of First Peoples, as well as other vulnerable and marginalized communities.
  • Identify and develop innovative ways of producing and disseminating research that better meet the expectations of these communities.
  • Define inter-regroupement collaboration practices to fulfill R5’s transversal function.

Research Axes

Axis 1: Observing, anticipating, imagining and shaping ethical and responsible AI

In the manner of an observatory, this area includes both a research focus and a forward-looking scientific monitoring function. It will enable us to envision the various trajectories that ethical and responsible AI could take in the years to come, while delving deeper into the role of AI in transforming society.

Axis 2: Engagement, empowerment, self-determination and power issues related to AI

Axis 2 focuses on issues of power and digital sovereignty. It aims to promote a better understanding of AI (AI literacy), develop critical thinking about these technologies, and promote the empowerment and self-determination of the various stakeholders.

Axis 3: Ethical foundations, development and responsible implementation of AI

Axis 3 addresses both fundamental theoretical questions on the ethics of AI and the practical conditions necessary for its responsible deployment in organizations and society.

Challenges

The main challenge is to reconcile the speed of AI technological development with the ethical and social imperatives highlighted by the work of this group. Engagement with First Peoples must be based on the development of relationships of trust, which can take time to build.  AI also raises complex questions: how can we prevent systems from reproducing or amplifying existing biases? How can we ensure that marginalized communities are actively involved in the decisions that concern them?

It is also a question of preventing the growth of digital inequalities and establishing solid governance mechanisms. This challenge requires a multidisciplinary approach, inclusive methodologies and solutions that anticipate long-term impacts to shape a truly responsible AI.

Anticipated Impact

  • Foster the ethical and responsible development and deployment of AI.
  • Improving trust in AI: Promoting systems that are more explainable, accessible and respectful of human rights.
  • Strengthening social and cultural inclusion: Developing AI that values diversity and responds to the needs of different communities, including marginalized groups and First Peoples.
  • Reducing technological inequalities: Fostering an equitable digital transition that mitigates bias and democratizes access to the benefits of AI.

Supporting organizations: providing knowledge, tools and strategies for the development, implementation and monitoring of ethical and responsible AI technologies.

Research Team

Co-leaders

Joé T. Martineau
HEC Montréal
Annie Pullen Sansfaçon
Université de Montréal
Daniel Weinstock
McGill University

Researchers

Students

  • Aditi Khandelwal – McGill University
  • Alice Drely – HEC Montréal
  • Anne-Marie Ouellet – Université de Montréal
  • Anne Fleischman – Université de Montréal
  • Daniella Landrys – Université du Québec à Montréal
  • Florian Carichon – McGill University
  • Jean Frantz Ricardeau Registre – Université de Montréal
  • Jean-Frédérick Labranche – HEC Montréal
  • Jo-Ann Johnson – Université de Montréal
  • Meghana Bhange – Université de Montréal
  • Mina Alfaghih – Université de Montréal
  • Morgane Gelly – Université de Montréal

Research Advisor

Florence Lussier-Lejeune: florence.lussier-lejeune@ivado.ca

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