• 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

Research Summary: Trust and Transparency in Contact Tracing Applications

June 28, 2020

Summary contributed by Allison Cohen, a researcher here at MAIEI. Also consultant at AI Global. Previously an AI Strategy Consultant at Deloitte.

*Author & link to original paper at the bottom.


In a matter of days, a contact tracing application will be deployed in Ontario. It is estimated that 50-60% of the population must use the app in order for it to work as intended, warning individuals of exposure to COVID-19. But, how much do we really know about this technology? Of course, automatic contact tracing can be more accurate, efficient and comprehensive when identifying and notifying individuals who have been exposed to the virus relative to manual contact tracing; but, what are the trade-offs of this solution? To guide our thinking, authors of “Trust and Transparency in Contact Tracing Applications” have developed FactSheets, a list of questions users should consider before downloading a contact tracing application.

According to the article, users should begin by asking which technology the app uses to track users’ location. If the app uses GPS, it works by identifying a user’s geographical location and pairing that data with a timestamp. In terms of efficacy, the technology is impeded when users are indoors or in a building with different stories (e.g. an apartment building). Bluetooth, on the other hand, establishes contact events through proximity detection.

However, Bluetooth’s signal strength can be obstructed by the orientation of the device as well as the signal’s absorption into the human body, radio signals or in buildings and trains. Neither GPS nor Bluetooth capture variables such as ventilation or the use of masks and gloves, which also impact the likelihood of transmission. Not to mention, both technologies rest on assumptions that the device is in possession of one individual and stays with them at all times. Both of these assumptions can result in a false determination of exposure.

In addition to accuracy concerns, users should consider:

  • Privacy: sensitive data users are asked to share with the application (health status, location details, social interactions, name, gender, age, health history)
  • Security: the vulnerability of the application to attack
  • Coverage: the number of users that will opt into the use of the application
  • Accessibility: whether the technology is accessible to the entire population (consider that 47% of people aged 65 and older do not have smartphones)
  • Accuracy: whether the limitations of Bluetooth and GPS location tracking will undermine the accuracy of the app
  • Asynchronous Contact Events: whether the app will capture risk of exposure from transmission in circumstances other than proximity to others (i.e. infected surfaces)
  • Device Impacts: the app’s impact on the users’ devices (battery life etc.)
  • Ability: users’ capacity to use the app as intended
  • Ability: interoperability between contact tracing applications downloaded by the rest of the population
  • Reluctance in Disclosure: whether users will submit information about their positive COVID-19 diagnosis

Check out FactSheets to obtain further details users should consider before downloading a contact tracing application.


Original paper by Stacy Hobson, Michael Hind, Aleksandra Mojsilovic´ and Kush R. Varshney: https://arxiv.org/pdf/2006.11356.pdf

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

  • A Holistic Assessment of the Reliability of Machine Learning Systems

    A Holistic Assessment of the Reliability of Machine Learning Systems

  • The Values Encoded in Machine Learning Research

    The Values Encoded in Machine Learning Research

  • Confidence-Building Measures for Artificial Intelligence

    Confidence-Building Measures for Artificial Intelligence

  • Research summary: Social Work Thinking for UX and AI Design

    Research summary: Social Work Thinking for UX and AI Design

  • Investing in AI for Social Good: An Analysis of European National Strategies

    Investing in AI for Social Good: An Analysis of European National Strategies

  • The Ethical AI Startup Ecosystem 02: Data for AI

    The Ethical AI Startup Ecosystem 02: Data for AI

  • Clueless AI: Should AI Models Report to Us When They Are Clueless?

    Clueless AI: Should AI Models Report to Us When They Are Clueless?

  • A Sequentially Fair Mechanism for Multiple Sensitive Attributes

    A Sequentially Fair Mechanism for Multiple Sensitive Attributes

  • CRUSH: Contextually Regularized and User Anchored Self-Supervised Hate Speech Detection

    CRUSH: Contextually Regularized and User Anchored Self-Supervised Hate Speech Detection

  • Unlocking Accuracy and Fairness in Differentially Private Image Classification

    Unlocking Accuracy and Fairness in Differentially Private Image Classification

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.