arXiv:2605.27395v1 Announce Type: cross Abstract: As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has become increasingly challenging for policymakers and researchers. We introduce a methodology for identifying viable policy options to mitigate specified AI harms, helping policymakers and researchers target areas that warrant greater time and resource investment. This method combines participatory evaluation of policies, expert assessment of implementation costs, and an LLM-based assessment of perce
The rapid deployment of AI systems and documented harms necessitates urgent and effective policy interventions, driving the development of methodologies to prioritize these efforts.
This development offers a systematic approach to assess and prioritize AI policy options, crucial for guiding regulatory efforts and resource allocation in a complex, fast-evolving domain.
Policymakers and researchers now have a structured methodology, combining expert input and LLM-based analysis, to target AI governance areas more effectively, moving beyond ad-hoc decision-making.
- · AI policymakers
- · AI researchers
- · Governments implementing AI policies
- · Companies seeking regulatory clarity
- · Untested or poorly designed AI policy initiatives
- · Organizations relying on opaque policy processes
More informed and effective AI policies are developed, leading to reduced AI-related harms.
Globally, AI policy frameworks converge on best practices identified through robust assessment methodologies.
The structured assessment of AI policy influences the design of AI systems themselves, pushing developers towards 'governance-by-design' principles.
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Read at arXiv cs.AI