• Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • Core Principles of Responsible AI
    • Accountability
    • Fairness
    • Privacy
    • Safety and Security
    • Sustainability
    • Transparency
  • Special Topics
    • AI in Industry
    • Ethical Implications
    • Human-Centered Design
    • Regulatory Landscape
    • Technical Methods
  • Living Dictionary
  • State of AI Ethics
  • AI Ethics Brief
  • 🇫🇷
Montreal AI Ethics Institute

Montreal AI Ethics Institute

Democratizing AI ethics literacy

Report on Publications Norms for Responsible AI

September 18, 2020

Get the paper in PDF formDownload

This work is licensed under a ​Creative Commons Attribution 4.0 International License.


Based on insights and analysis by the Montreal AI Ethics Institute (MAIEI) staff and supplemented by workshop contributions from the AI Ethics community co-hosted by MAIEI & Partnership on AI on May 13th, 2020, and May 20th, 2020.

Abstract

The history of science and technology shows that seemingly innocuous developments in scientific theories and research have enabled real-world applications with significant negative consequences for humanity. In order to ensure that the science and technology of AI is developed in a humane manner, we must develop research publication norms that are informed by our growing understanding of AI’s potential threats and use cases. Unfortunately, it’s difficult to create a set of publication norms for responsible AI because the field of AI is currently fragmented in terms of how this technology is researched, developed, funded, etc. To examine this challenge and find solutions, the Montreal AI Ethics Institute (MAIEI) co-hosted two public consultation meetups with the Partnership on AI in May 2020. These meetups examined potential publication norms for responsible AI, with the goal of creating a clear set of recommendations and ways forward for publishers.

In its submission, MAIEI provides six initial recommendations, these include: 1) create tools to navigate publication decisions, 2) offer a page number extension, 3) develop a network of peers, 4) require broad impact statements, 5) require the publication of expected results, and 6) revamp the peer-review process. After considering potential concerns regarding these recommendations, including constraining innovation and creating a “black market” for AI research, MAIEI outlines three ways forward for publishers, these include: 1) state clearly and consistently the need for established norms, 2) coordinate and build trust as a community, and 3) change the approach.

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

AI Policy Corner: Texas and New York: Comparing U.S. State-Level AI Laws

What is Sovereign Artificial Intelligence?

AI Policy Corner: The Kenya National AI Strategy

AI Policy Corner: New York City Local Law 144

Canada’s Minister of AI and Digital Innovation is a Historic First. Here’s What We Recommend.

related posts

  • A Critical Analysis of the What3Words Geocoding Algorithm

    A Critical Analysis of the What3Words Geocoding Algorithm

  • A Snapshot of the Frontiers of Fairness in Machine Learning (Research Summary)

    A Snapshot of the Frontiers of Fairness in Machine Learning (Research Summary)

  • Not Quite ‘Ask a Librarian’: AI on the Nature, Value, and Future of LIS

    Not Quite ‘Ask a Librarian’: AI on the Nature, Value, and Future of LIS

  • Research summary: Algorithmic Accountability

    Research summary: Algorithmic Accountability

  • Moral Dilemmas for Moral Machines

    Moral Dilemmas for Moral Machines

  • Language (Technology) is Power: A Critical Survey of “Bias” in NLP (Research summary)

    Language (Technology) is Power: A Critical Survey of “Bias” in NLP (Research summary)

  • Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentati...

    Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentati...

  • Balancing Data Utility and Confidentiality in the 2020 US Census

    Balancing Data Utility and Confidentiality in the 2020 US Census

  • Research summary: Snapshot Series: Facial Recognition Technology

    Research summary: Snapshot Series: Facial Recognition Technology

  • Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection

    Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection

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

  • © MONTREAL AI ETHICS INSTITUTE. All rights reserved 2024.
  • 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.