Human and Organizational Factors in AI Risk Management Proceedings of a Workshop (2025) / Chapter Skim
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1 Widening Participation in the Design, Development, and Deployment of AI Tools
1 Widening Participation in the Design, Development, and Deployment of AI Tools
Pages 1-9
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, and field testing (e.g., "several thousands of human participants interacting with AI applications in realistic settings") .1 Waters highlighted the importance of creating risk-aware organizational cultures and governance mechanisms to promote ethical AI practices, explainability, and accountability.
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These practices include evaluating system performance in the real world rather than in con trolled settings, considering both positive and negative potential societal impacts, and assessing AI systems proactively rather than reactively. Waters also pointed to tools and frameworks, including bias detection software, model auditing tools, and risk assess ment frameworks, as starting points for effective AI evaluation.
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Although they found that internally developed principles appear to perform well, Ganguli's team recognized the importance of external input and began using a tool called Polis3 that allowed for public input. Polis allows members of the public to suggest and deliberate on possible normative rules, and the developers use this input to fashion appropriate new rules to train the AI models further.
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With respect to low-resource language communities, Hecht noted that such communities may have more resources to invest in meeting their needs through AI than is currently appreciated but cautioned that they are concerned about preserving the identity and provenance of their language. 4 Crowd workers perform tasks via online platforms such as Amazon Mechanical Turk and Polis.
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Dubal pointed to the decline in union labor representation as well as gaps in the protections accorded to gig workers as obstacles to risk mitigation. The European Union recently enacted a directive called the European Union Artificial Intelligence Act, high lighted by Dubal, that enhances the rights of people who work through platforms by D e s i g n , D e ve l o p m e n t , a n d D e p l o y m e n t o f A I To o l s 5
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Colclough cautioned that risk management frameworks, such as those developed by NIST and the European Union, can only go so far in addressing risks to workers given that some of the risks ultimately stem from shortcomings in basic protections of worker rights. Kauffman stated that national legislation and labor organization efforts can still fall short of protecting global employees of multinational corporations because compa nies can and do shift work to countries with weaker worker protection.
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The law requires that employees are provided advance notice of their quotas so that AI tools cannot be used to base future expectations on their rate of work.6 PANEL 3: BUILDING EQUITABLE INFRASTRUCTURE TO ADDRESS RISK The third panel was moderated by Sheena Erete, University of Maryland, and centered on the tools and systems needed to diversify participation in the design of AI tools to better address risks to those who have traditionally been marginalized. Erete opened the panel by asking how one might define or view such infrastructure.
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Washington discussed an ongoing project focused on building an African American Vernacular English data consortium to collect, store, and protect audio and other information about African American language and culture. Washington noted initial resource challenges such as lack of cloud infrastructure and funding to pay participants, which constrained not only developing the project but also building trust with participants.
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Park suggested strong whistleblower protection as another type of risk mitigation infrastructure. Park and El Zein expressed concerns about the inherent risks of certain applications of AI.
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