arXiv:2605.30995v1 Announce Type: cross Abstract: Public consultations generate large volumes of data in the form of stakeholder submissions that are practically unfeasible to analyse manually. We present an end-to-end LLM-based pipeline and interactive dashboard for structured topic extraction from regulatory consultation submissions, demonstrated on the European Commission's Digital Fairness Act (DFA) public call for evidence as a case study. The system processes raw PDF attachments and web-form responses, extracts topic annotations, and grounds every extraction in a verbatim quote from the
The proliferation of LLMs and increasing regulatory complexity around AI necessitate automated tools for policy analysis, making this a timely development.
This development indicates a tangible application of AI to streamline regulatory processes, potentially accelerating policy formation and implementation, particularly concerning AI itself.
The ability of regulatory bodies to process and analyze public feedback will be significantly enhanced, shifting from manual review to AI-assisted analysis.
- · Regulatory bodies
- · AI developers
- · Policy makers
- · Consulting firms
- · Manual data analysts
- · Legacy policy analysis software
Regulatory bodies can process public consultations much faster and more comprehensively.
This leads to more data-driven and potentially more efficient policy decisions, especially in fast-evolving sectors like AI.
The development could set a precedent for AI-driven policy analysis becoming standard, influencing the speed and nature of global regulatory frameworks.
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Read at arXiv cs.CL