Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making
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Abstract (arXiv)
The potential for negative impacts of AI has rapidly become more pervasive around the world, and this has intensified a need for responsible AI governance. While many regulatory bodies endorse risk-based approaches and a multitude of risk mitigation practices are proposed by companies and academic scholars, these approaches are commonly expert-centered and thus lack the inclusion of a significant group of stakeholders. Ensuring that AI policies align with democratic expectations requires methods that prioritize the voices and needs of those impacted. In this work we develop a participative and forward-looking approach to inform policy-makers and academics that grounds the needs of lay stakeholders at the forefront and enriches the development of risk mitigation strategies. Our approach (1) maps potential mitigation and prevention strategies of negative AI impacts that assign responsibility to various stakeholders, (2) explores the importance and prioritization thereof in the eyes of laypeople, and (3) presents these insights in policy fact sheets, i.e., a digestible format for informing policy processes. We emphasize that this approach is not targeted towards replacing policy-makers; rather our aim is to present an informative method that enriches mitigation strategies and enables a more participatory approach to policy development.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 70 references that are in the map. First posted on arXiv 24 Jan 2025.
Builds on
22 of 70 references.
References · 22
- 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
- 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 137
- 2019Principles alone cannot guarantee ethical AIcited by 134
- 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 91
- 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
- 2021Beyond Fair Pay: Ethical Implications of NLP Crowdsourcingcited by 52
- 2017Society-in-the-Loop: Programming the Algorithmic Social Contractcited by 35
- 2024AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policiescited by 34
- 2021Unpacking the Expressed Consequences of AI Research in Broader Impact Statementscited by 22
- 2023The Ethical Implications of Generative Audio Models: A Systematic Literature Reviewcited by 18
- 2024The Social Impact of Generative AI: An Analysis on ChatGPTcited by 18
- 2024ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologiescited by 12
10 more
- 2019U.S. Public Opinion on the Governance of Artificial Intelligencecited by 11
- 2024Envisioning the Applications and Implications of Generative AI for News Mediacited by 11
- 2022Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Makingcited by 9
- 2024Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil societycited by 7
- 2023Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environmentcited by 6
- 2024Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulationcited by 5
- 2023Auditing Recommender Systems -- Putting the DSA into practice with a risk-scenario-based approachcited by 4
- 2022Normative Challenges of Risk Regulation of Artificial Intelligence and Automated Decision-Makingcited by 3
- 2024Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrationscited by 3
- 2024Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AIcited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.