Responsible Artificial Intelligence (RAI) is a six-year multidisciplinary, multi-sector training initiative to build sustainable connections, research, training and knowledge capacity and a pipeline of highly qualified trainees in Canada’s fastest-growing knowledge economy sector. RAI is part of the NSERC Collaborative Research and Training Experience (CREATE) program.


Changing the way AI training is delivered in Canada

Are you looking to boost efficiency, promote corporate and community goals and credibility and comply with international privacy laws and regulations? RAI will fulfill these goals, ethical and moral obligations and the interests of those seeking a competitive advantage by shaping and building the next generation of responsible AI experts.


Areas of Thematic Research

By focusing on the key research areas of AI ethics by design, privacy-enhanced analytics, and AI accountability, the unique interdisciplinary structure of RAI allows for an unbiased approach to the responsible development of AI without undue sector influence and with relevance to government, industry and civil society stakeholders.

  • AI Ethics by Design

With AI’s potential to impact millions of people, it is imperative for developers and implementers to understand its ethical implications regarding privacy, transparency and bias. AI ethics by design research enables industries to shape their processes, guidelines and governance structures needed to achieve responsible AI.

Key researchers include Robin Cohen, Maura Grossman, Reihaneh Rabbany, and Joanna Redden.


  • Privacy-enhanced Analytics

Research in this area allows for personalized responsible AI applications that enhance both user experience and society and achieve stakeholders’ goals. In addition, this research offers accelerated safe data collection and collaboration and maximized data value without compromising consumer privacy. 

Key researchers include Benjamin Fung, Sébastien Gambs, Qinmin (Vivian) Hu and Zack Marshall.


  • AI Accountability

With the proliferation of AI applications in high-stakes decision-making contexts, there is an urgent need to address AI accountability questions and establish AI’s trustworthiness. AI accountability research allows future AI experts to collaborate across disciplines to recognize and eliminate societal threats, challenges and negative impacts of AI innovations.

Key researchers include Faezeh Ensan, Frauke Zeller, Robin Cohen and Maura Grossman.



    Cours disponibles

    All Events 2024

    Communicating Knowledge 2023

    AI Ethics Debates

    Responsible AI 2023 Lectures

    Invited Speaker Series 22/23

    Communicating complexity and knowledge mobilization

    NOTE: This series is mandatory for students completing the RAI training program.

    This series of workshops will provide students with a background on how to share technically complex research with non-technical audiences. Using examples and activities from the world of artificial intelligence

    students will learn how to write, present and share important academic research with non-academic audiences. 

    The class will consist of four, 2 hour live streamed classes which will feature a combination of lecture and in-class activities. Each week may feature supplementary reading and video material that students will engage with in advance of the class.

    Class Schedule:

    Class 1 - Friday May 13th 12 PM - 2PM : Class introduction background on communicating complexity

    Class 2 - Friday May 20th 12 PM - 2PM :  How to present complex topics to non-technical audiences

    Class 3 - Friday May 27th 12 PM - 2PM :  How to write about complex audience for non technical audiences

    Class 4 - Friday June 10th - 12 PM - 2PM : Effective knowledge mobilization using social media and digital platforms


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    Course Lectures for 2021-2022

    Lectures led by program PI's