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When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines.

Sachin R Pendse1, Darren Gergle2, Rachel Kornfield1

  • 1Northwestern University, Chicago, Illinois, USA.

Proceedings of the ... Conference on Fairness, Accountability, and Transparency
|June 18, 2026
PubMed
Summary

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AI red-teaming, crucial for AI safety, causes mental health harm. This study highlights unmet needs and proposes strategies to protect red-teamers

Area of Science:

  • Artificial Intelligence Safety
  • Human-Computer Interaction
  • Occupational Health Psychology

Background:

  • Generative AI models require systematic testing (red-teaming) to prevent harmful content.
  • The "black box" nature of AI necessitates interactional testing, where testers simulate malicious actors.
  • This adversarial engagement, termed "interactional labor," poses unique mental health risks to red-teamers.

Purpose of the Study:

  • To identify and analyze the specific mental health harms experienced by AI red-teamers.
  • To propose individual and organizational strategies for safeguarding red-teamers' mental wellbeing.
  • To address the critical workplace safety concern of unmet mental health needs in AI safety professionals.

Main Methods:

  • Analysis of the unique mental health impacts of red-teaming interactional labor.
Keywords:
AI safetygenerative artificial intelligencemental healthred-teaming

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  • Drawing parallels between red-teaming practices and interactional labor in other professions (e.g., actors, content moderators).
  • Developing adaptive strategies based on existing mental health safeguards from comparable professions.

    • Main Results:

    • Red-teaming involves psychological demands that can lead to significant mental health harms.
    • Existing professional practices offer models for mitigating these harms.
    • Unmet mental health needs represent a critical gap in current AI safety infrastructure.

    Conclusions:

    • Protecting the mental health of AI red-teamers is essential for robust AI safety.
    • Implementing tailored individual and organizational safeguards is crucial.
    • Addressing these needs is vital for the sustainability of AI safety efforts on the "digital frontlines."