• Skip to main content
  • Skip to secondary menu
  • Skip to primary sidebar
  • Skip to footer
Montreal AI Ethics Institute

Montreal AI Ethics Institute

Democratizing AI ethics literacy

  • Articles
    • Public Policy
    • Privacy & Security
    • Human Rights
      • Ethics
      • JEDI (Justice, Equity, Diversity, Inclusion
    • Climate
    • Design
      • Emerging Technology
    • Application & Adoption
      • Health
      • Education
      • Government
        • Military
        • Public Works
      • Labour
    • Arts & Culture
      • Film & TV
      • Music
      • Pop Culture
      • Digital Art
  • Columns
    • AI Policy Corner
    • Recess
  • The AI Ethics Brief
  • AI Literacy
    • Research Summaries
    • AI Ethics Living Dictionary
    • Learning Community
  • The State of AI Ethics Report
    • Volume 7 (November 2025)
    • Volume 6 (February 2022)
    • Volume 5 (July 2021)
    • Volume 4 (April 2021)
    • Volume 3 (Jan 2021)
    • Volume 2 (Oct 2020)
    • Volume 1 (June 2020)
  • About
    • Our Contributions Policy
    • Our Open Access Policy
    • Contact
    • Donate

The Social Contract for AI

June 17, 2020

Full paper in PDF formDownload

Authors: Mirka Snyder Caron, Abhishek Gupta

Abstract

Like any technology, AI systems come with inherent risks and potential benefits. It comes with potential disruption of established norms and methods of work, societal impacts and externalities. One may think of the adoption of technology as a form of social contract, which may evolve or fluctuate in time, scale, and impact. It is important to keep in mind that for AI, meeting the expectations of this social contract is critical, because recklessly driving the adoption and implementation of unsafe, irresponsible, or unethical AI systems may trigger serious backlash against industry and academia involved which could take decades to resolve, if not actually seriously harm society.

For the purpose of this paper, we consider that a social contract arises when there is sufficient consensus within society to adopt and implement this new technology. As such, to enable a social contract to arise for the adoption and implementation of AI, developing: 1) A socially accepted purpose, through 2) A safe and responsible method, with 3) A socially aware level of risk involved, for 4) A socially beneficial outcome, is key.

Full paper in PDF formDownload
Want quick summaries of the latest research & reporting in AI ethics delivered to your inbox? Subscribe to the AI Ethics Brief. We publish bi-weekly.

Primary Sidebar

🔍 SEARCH

Spotlight

ALL IN Conference 2025: Four Key Takeaways from Montreal

Beyond Dependency: The Hidden Risk of Social Comparison in Chatbot Companionship

AI Policy Corner: Restriction vs. Regulation: Comparing State Approaches to AI Mental Health Legislation

Beyond Consultation: Building Inclusive AI Governance for Canada’s Democratic Future

AI Policy Corner: U.S. Executive Order on Advancing AI Education for American Youth

related posts

  • Conversational AI Systems for Social Good: Opportunities and Challenges

    Conversational AI Systems for Social Good: Opportunities and Challenges

  • How Machine Learning Can Enhance Remote Patient Monitoring

    How Machine Learning Can Enhance Remote Patient Monitoring

  • Promises and Challenges of Causality for Ethical Machine Learning

    Promises and Challenges of Causality for Ethical Machine Learning

  • Bridging the Gap Between AI and the Public (TEDxYouth@GandyStreet)

    Bridging the Gap Between AI and the Public (TEDxYouth@GandyStreet)

  • The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms (Research Summary)

    The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms (Research Summary)

  • The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommend...

    The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommend...

  • AI Governance on the Ground: Canada’s Algorithmic Impact Assessment Process and Algorithm has evolve...

    AI Governance on the Ground: Canada’s Algorithmic Impact Assessment Process and Algorithm has evolve...

  • Responsible AI In Healthcare

    Responsible AI In Healthcare

  • Tiny, Always-on and Fragile: Bias Propagation through Design Choices in On-device Machine Learning W...

    Tiny, Always-on and Fragile: Bias Propagation through Design Choices in On-device Machine Learning W...

  • On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise

    On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise

Partners

  •  
    U.S. Artificial Intelligence Safety Institute Consortium (AISIC) at NIST

  • Partnership on AI

  • The LF AI & Data Foundation

  • The AI Alliance

Footer


Articles

Columns

AI Literacy

The State of AI Ethics Report


 

About Us


Founded in 2018, the Montreal AI Ethics Institute (MAIEI) is an international non-profit organization equipping citizens concerned about artificial intelligence and its impact on society to take action.

Contact

Donate


  • © 2025 MONTREAL AI ETHICS INSTITUTE.
  • This work is licensed under a Creative Commons Attribution 4.0 International License.
  • Learn more about our open access policy here.
  • Creative Commons License

    Save hours of work and stay on top of Responsible AI research and reporting with our bi-weekly email newsletter.