# Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making

arXiv: https://arxiv.org/abs/2502.14869 · HTML: https://mlsanity.com/paper/2502.14869/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 70 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 22 of 22)

- [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Evaluating the Social Impact of Generative AI Systems in Systems and Society](https://mlsanity.com/paper/2306.05949/) (2023)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing](https://mlsanity.com/paper/2104.10097/) (2021)
- [AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies](https://mlsanity.com/paper/2406.17864/) (2024)
- [Society-in-the-Loop: Programming the Algorithmic Social Contract](https://mlsanity.com/paper/1707.07232/) (2017)
- [Unpacking the Expressed Consequences of AI Research in Broader Impact Statements](https://mlsanity.com/paper/2105.04760/) (2021)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [The Social Impact of Generative AI: An Analysis on ChatGPT](https://mlsanity.com/paper/2403.04667/) (2024)
- [ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologies](https://mlsanity.com/paper/2407.12454/) (2024)
- [U.S. Public Opinion on the Governance of Artificial Intelligence](https://mlsanity.com/paper/1912.12835/) (2019)
- [Envisioning the Applications and Implications of Generative AI for News Media](https://mlsanity.com/paper/2402.18835/) (2024)
- [Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making](https://mlsanity.com/paper/2207.09525/) (2022)
- [Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society](https://mlsanity.com/paper/2403.07904/) (2024)
- [Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment](https://mlsanity.com/paper/2310.06361/) (2023)
- [Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation](https://mlsanity.com/paper/2405.09679/) (2024)
- [Auditing Recommender Systems -- Putting the DSA into practice with a risk-scenario-based approach](https://mlsanity.com/paper/2302.04556/) (2023)
- [Normative Challenges of Risk Regulation of Artificial Intelligence and Automated Decision-Making](https://mlsanity.com/paper/2211.06203/) (2022)
- [Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrations](https://mlsanity.com/paper/2401.13605/) (2024)
- [Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AI](https://mlsanity.com/paper/2410.23704/) (2024)

## Built on by (most cited first, 2 of 2)

- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)
- [Informing AI Policy Assessment using Large-Scale Simulation of Interventions](https://mlsanity.com/paper/2605.27395/) (2026)

## 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.