• 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

Participatory Design to build better contact- and proximity-tracing apps

June 4, 2020

Full paper in PDF formDownload

Below is the abstract from the full paper, authored by Abhishek Gupta and Tania De Gasperis. (equal contributions from each author)

Abstract

With the push for contact- and proximity-tracing solutions as a means to manage the spread of the pandemic, there is a distrust between the citizens and authorities that are deploying these solutions. The efficacy of the solutions relies on meeting a minimum uptake threshold which is hitting a barrier because of a lack of trust and transparency in how these solutions are being developed.

We propose participatory design as a mechanism to evoke trust and explore how it might be applied to co-create technological solutions that not only meet the needs of the users better but also expand their reach to underserved and high-risk communities. We also highlight the role of the bazaar model of development and complement that with quantitative and qualitative metrics for evaluating the solutions and convincing policymakers and other stakeholders in the value of this approach with empirical evidence.

Keywords: participatory design, contact tracing, proximity tracing, trust, transparency

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

Agentic AI systems and algorithmic accountability: a new era of e-commerce

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

related posts

  • Talking About Large Language Models

    Talking About Large Language Models

  • Attacking Fake News Detectors via Manipulating News Social Engagement

    Attacking Fake News Detectors via Manipulating News Social Engagement

  • The Moral Machine Experiment on Large Language Models

    The Moral Machine Experiment on Large Language Models

  • Study of Competition Issues in Data-Driven Markets in Canada

    Study of Competition Issues in Data-Driven Markets in Canada

  • Auditing for Human Expertise

    Auditing for Human Expertise

  • Going public: the role of public participation approaches in commercial AI labs

    Going public: the role of public participation approaches in commercial AI labs

  • Mapping value sensitive design onto AI for social good principles

    Mapping value sensitive design onto AI for social good principles

  • Ethics of AI in Education: Towards a Community-wide Framework

    Ethics of AI in Education: Towards a Community-wide Framework

  • Towards Sustainable Conversational AI

    Towards Sustainable Conversational AI

  • Zoom Out and Observe: News Environment Perception for Fake News Detection

    Zoom Out and Observe: News Environment Perception for Fake News Detection

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.