• 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

Report on the Santa Clara Principles ​for Content Moderation

July 3, 2020

Get the paper in PDF formDownload

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


Context: In April 2020, the Electronic Frontier Foundation (EFF) publicly called for comments on expanding and improving the Santa Clara Principles on Transparency and Accountability (SCP), originally published in May 2018. The Montreal AI Ethics Institute (MAIEI) responded to this call by drafting a set of recommendations based on insights and analysis by the MAIEI staff and supplemented by workshop contributions from the AI Ethics community convened during two online public consultation meetups.

Overview of our recommendations

● There should be more diversity in the content moderation process. Potential biases and discriminatory decisions constitute a great concern for content moderation whether performed by a human or machine.

● There is a need for transparency concerning how platforms guide content-ranking, which has the potential to restrict freedom of expression and users’ autonomy, and stifle social change.

● Anonymized data on the training and/or cultural background of the content moderators employed by a platform should be disclosed.

● There are no one-size-fits-all solutions: content moderation tools must be tailored to specific issues. For instance, misinformation may be best addressed through behavioral nudges, whereas hate speech may require more drastic measures. Guidelines to address all the possible types of content moderation tools employed on online platforms are necessary.

● Specific guidelines are needed for messaging applications with regards to data protection in content moderation.

● Cultural differences relevant to what constitutes acceptable behavior online need to be taken into account in content moderation.

● When it comes to political advertising, we need to make sure that platforms are transparent. Integrity policies for political content should be the same as the policies adopted for other types of content.

● The flagging/reporting system provided to users by platforms would benefit from greater transparency, as it may be particularly problematic when used in contexts where the majority of users are prone to discriminate against minority groups.

● When user content is flagged or reported, it must be clear when the flagging and reporting is automated.

● More data should be made available on the types of content removed from platforms online to make this process more transparent.

● Platforms should provide clear guidelines on the appeal process, as well as data on prior appeals. The appeal process should also be intelligible to a layperson, and not make one feel as though they must seek external legal counsel to navigate said process.

● We believe the Principles should be periodically revisited — for instance, every two years — or within a timeframe that allows for any appropriate revisions. This would allow the Principles to reflect various technological advancements, modifications in law and policy, as well as changing trends or movements in terms of platforms’ content moderation.

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

  • A Lesson From AI: Ethics Is Not an Imitation Game

    A Lesson From AI: Ethics Is Not an Imitation Game

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

    Research summary: Social Work Thinking for UX and AI Design

  • Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarl...

    Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarl...

  • Positive AI Economic Futures: Insight Report

    Positive AI Economic Futures: Insight Report

  • U.S.-EU Trade and Technology Council Inaugural Joint Statement – A look into what’s in store for AI?

    U.S.-EU Trade and Technology Council Inaugural Joint Statement – A look into what’s in store for AI?

  • Regulating AI to ensure Fundamental Human Rights: reflections from the Grand Challenge EU AI Act

    Regulating AI to ensure Fundamental Human Rights: reflections from the Grand Challenge EU AI Act

  • AI Consent Futures: A Case Study on Voice Data Collection with Clinicians

    AI Consent Futures: A Case Study on Voice Data Collection with Clinicians

  • Incentivized Symbiosis: A Paradigm for Human-Agent Coevolution

    Incentivized Symbiosis: A Paradigm for Human-Agent Coevolution

  • FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation (NeurIPS 2024)

    FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation (NeurIPS 2024)

  • Breaking Fair Binary Classification with Optimal Flipping Attacks

    Breaking Fair Binary Classification with Optimal Flipping Attacks

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.