Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making
- URL: http://arxiv.org/abs/2502.14869v1
- Date: Fri, 24 Jan 2025 22:57:18 GMT
- Title: Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making
- Authors: Julia Barnett, Kimon Kieslich, Natali Helberger, Nicholas Diakopoulos,
- Abstract summary: The potential for negative impacts of AI has rapidly become more pervasive around the world.<n>This has intensified a need for responsible AI governance.<n> Ensuring that AI policies align with democratic expectations requires methods that prioritize the voices and needs of those impacted.
- Score: 2.981139602986498
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
arXiv Detail & Related papers (2026-02-16T04:30:06Z) - Who Sees the Risk? Stakeholder Conflicts and Explanatory Policies in LLM-based Risk Assessment [7.295206349432321]
This paper presents a framework for stakeholder-grounded risk assessment by using LLMs.<n>Using the Risk Atlas Nexus and GloVE explanation method, our framework generates stakeholder-specific, interpretable policies.<n>We demonstrate our method using three real-world AI use cases of medical AI, autonomous vehicles, and fraud detection domain.
arXiv Detail & Related papers (2025-11-05T03:19:21Z) - "We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe [56.1653658714305]
We interviewed 25 AI developers based in Europe to understand which privacy threats they believe pose the greatest risk to users, developers, and businesses.<n>We find that there is little consensus among AI developers on the relative ranking of privacy risks.<n>While AI developers are aware of proposed mitigation strategies for addressing these risks, they reported minimal real-world adoption.
arXiv Detail & Related papers (2025-10-01T13:51:33Z) - Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
arXiv Detail & Related papers (2025-08-12T09:42:56Z) - Advancing Science- and Evidence-based AI Policy [163.43609502905707]
This paper tackles the problem of how to optimize the relationship between evidence and policy to address the opportunities and challenges of AI.<n>An increasing number of efforts address this problem by often either (i) contributing research into the risks of AI and their effective mitigation or (ii) advocating for policy to address these risks.
arXiv Detail & Related papers (2025-08-02T23:20:58Z) - Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.
Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
arXiv Detail & Related papers (2025-03-12T21:39:38Z) - Securing External Deeper-than-black-box GPAI Evaluations [49.1574468325115]
This paper examines the critical challenges and potential solutions for conducting secure and effective external evaluations of general-purpose AI (GPAI) models.
With the exponential growth in size, capability, reach and accompanying risk, ensuring accountability, safety, and public trust requires frameworks that go beyond traditional black-box methods.
arXiv Detail & Related papers (2025-03-10T16:13:45Z) - Global Perspectives of AI Risks and Harms: Analyzing the Negative Impacts of AI Technologies as Prioritized by News Media [3.2566808526538873]
AI technologies have the potential to drive economic growth and innovation but can also pose significant risks to society.<n>One way to understand these nuances is by looking at how the media reports on AI.<n>We analyze a broad and diverse sample of global news media spanning 27 countries across Asia, Africa, Europe, Middle East, North America, and Oceania.
arXiv Detail & Related papers (2025-01-23T19:14:11Z) - Towards Responsible Governing AI Proliferation [0.0]
The paper introduces the Proliferation' paradigm, which anticipates the rise of smaller, decentralized, open-sourced AI models.
It posits that these developments are both probable and likely to introduce both benefits and novel risks.
arXiv Detail & Related papers (2024-12-18T13:10:35Z) - Implications for Governance in Public Perceptions of Societal-scale AI Risks [0.29022435221103454]
Voters perceive AI risks as both more likely and more impactful than experts, and also advocate for slower AI development.
Policy interventions may best assuage collective concerns if they attempt to more carefully balance mitigation efforts across all classes of societal-scale risks.
arXiv Detail & Related papers (2024-06-10T11:52:25Z) - Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation [3.2566808526538873]
We use GPT-4 to generate scenarios both pre- and post-introduction of policy.
We then run a user study to evaluate these scenarios across four risk-assessment dimensions.
We find that this transparency legislation is perceived to be effective at mitigating harms in areas such as labor and well-being, but largely ineffective in areas such as social cohesion and security.
arXiv Detail & Related papers (2024-05-15T19:44:54Z) - LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models.
It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
arXiv Detail & Related papers (2024-04-01T16:50:54Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
General purpose AI seems to have lowered the barriers for the public to use AI and harness its power.
We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - On strategies for risk management and decision making under uncertainty shared across multiple fields [55.2480439325792]
The paper finds more than 110 examples of such strategies and this approach to risk is termed RDOT: Risk-reducing Design and Operations Toolkit.
RDOT strategies fall into six broad categories: structural, reactive, formal, adversarial, multi-stage and positive.
Overall, RDOT represents an overlooked class of versatile responses to uncertainty.
arXiv Detail & Related papers (2023-09-06T16:14:32Z) - On the Value of Myopic Behavior in Policy Reuse [67.37788288093299]
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
In this work, we present a framework called Selective Myopic bEhavior Control(SMEC)
SMEC adaptively aggregates the sharable short-term behaviors of prior policies and the long-term behaviors of the task policy, leading to coordinated decisions.
arXiv Detail & Related papers (2023-05-28T03:59:37Z) - Institutionalising Ethics in AI through Broader Impact Requirements [8.793651996676095]
We reflect on a novel governance initiative by one of the world's largest AI conferences.
NeurIPS introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research.
We investigate the risks, challenges and potential benefits of such an initiative.
arXiv Detail & Related papers (2021-05-30T12:36:43Z) - Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness [116.804536884437]
We propose an opposite behavior aware framework for policy learning in goal-oriented dialogues.
We estimate the opposite agent's policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy.
arXiv Detail & Related papers (2020-04-21T03:13:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.