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Privacy and ethics: Understanding the convergences and tensions for the responsible development of machine learning


Université du Québec à Montréal (UQAM)



Project Leader(s)

Sébastien Gambs


The success of machine learning models is such that they are now ubiquitous in our society. Their widespread use also raises serious privacy and ethical issues, however, especially if their predictions are put into action in domains in which they can significantly affect individuals. As a result, we have witnessed in recent years several initiatives proposing design principles and guidelines for the responsible development of artificial intelligence.

Nonetheless, very few works have explored the tensions but also the convergences that can emerge when addressing jointly the privacy and ethical challenges that go into designing and deploying machine learning models. Moreover, the key question is whether the achievement of these different objectives is always a positive sum game.

To understand how we may best address privacy and ethics responsibly when developing machine learning models, we therefore need to first have a clear view on how these concepts interact with each other in a positive as well as negative manner. The objective of the project is precisely to investigate this question by following an interdisciplinary approach at the crossroads of computer science, law and ethics.

Project deliverables are available in the following language(s)


OPC Funded Project

This project received funding support through the Office of the Privacy Commissioner of Canada’s Contributions Program. The opinions expressed in the summary and report(s) are those of the authors and do not necessarily reflect those of the Office of the Privacy Commissioner of Canada. Summaries have been provided by the project authors. Please note that the projects appear in their language of origin.

Contact Information

Sébastien Gambs
Université du Québec à Montréal
Informatics Department
P.O. Box 8888, Centertown Branch
Montreal, QC H3C 3P8
Telephone: (514) 987-3000, ext. 0906

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