• 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
      • JEDI (Justice, Equity, Diversity, Inclusion
      • Ethics
    • 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
    • AI Ethics Living Dictionary
    • Learning Community
  • The State of AI Ethics Report
    • 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

Trust me!: How to use trust-by-design to build resilient tech in times of crisis

July 28, 2020

Get the paper in PDF formDownload

*NOTE: This article was first published July 19, 2020, on Westlaw Practitioner Insights. Republished with permission.

By Gabrielle Paris Gagnon, Esq., and Vanessa Henri, Esq., Fasken, and Abhishek Gupta, Montreal AI Ethics Institute

Abstract

Nations across the world have started to deploy their own contact-and proximity tracing apps that claim to be able to balance the privacy and security of users’ data while helping to combat the spread of COVID-19, but do users trust them? The efficacy of such applications depends, among other things, on high adoption and consistent use rates, but this will be made difficult if users do not trust the tracing apps. Trust is a defining factor in the adoption of emerging technologies, and tracing apps are not an exception. In this article, we argue that trust-based design is critical to the development of technologies and use of data during crisis such as the COVID-19 pandemic. Trust helps to maintain social cohesion by hindering misinformation and allowing for a collective response.


Get the 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

  • Bias and Fairness in Large Language Models: A Survey

    Bias and Fairness in Large Language Models: A Survey

  • A Machine Learning Challenge or a Computer Security Problem?

    A Machine Learning Challenge or a Computer Security Problem?

  • Putting AI ethics to work: are the tools fit for purpose?

    Putting AI ethics to work: are the tools fit for purpose?

  • How to invest in Data and AI companies responsibly

    How to invest in Data and AI companies responsibly

  • Anthropomorphism and the Social Robot

    Anthropomorphism and the Social Robot

  • Applying the TAII Framework on Tesla Bot

    Applying the TAII Framework on Tesla Bot

  • Responsible Use of Technology in Credit Reporting: White Paper

    Responsible Use of Technology in Credit Reporting: White Paper

  • The Return on Investment in AI Ethics: A Holistic Framework

    The Return on Investment in AI Ethics: A Holistic Framework

  • Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learn...

    Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learn...

  • A Hazard Analysis Framework for Code Synthesis Large Language Models

    A Hazard Analysis Framework for Code Synthesis Large Language Models

Partners

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

  • Partnership on AI

  • The LF AI & Data Foundation

  • The AI Alliance

Footer

Categories


• Blog
• Research Summaries
• Columns
• Core Principles of Responsible AI
• Special Topics

Signature Content


• The State Of AI Ethics

• The Living Dictionary

• The AI Ethics Brief

Learn More


• About

• Open Access Policy

• Contributions Policy

• Editorial Stance on AI Tools

• Press

• Donate

• Contact

The AI Ethics Brief (bi-weekly newsletter)

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.


Archive

  • © 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.