{"ID":2871743,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10104","arxiv_id":"2509.10104","title":"AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework","abstract":"The absolute dominance of Artificial Intelligence (AI) introduces unprecedented societal harms and risks. Existing AI risk assessment models focus on internal compliance, often neglecting diverse stakeholder perspectives and real-world consequences. We propose a paradigm shift to a human-centric, harm-severity adaptive approach grounded in empirical incident data. We present AI Harmonics, which includes a novel AI harm assessment metric (AIH) that leverages ordinal severity data to capture relative impact without requiring precise numerical estimates. AI Harmonics combines a robust, generalized methodology with a data-driven, stakeholder-aware framework for exploring and prioritizing AI harms. Experiments on annotated incident data confirm that political and physical harms exhibit the highest concentration and thus warrant urgent mitigation: political harms erode public trust, while physical harms pose serious, even life-threatening risks, underscoring the real-world relevance of our approach. Finally, we demonstrate that AI Harmonics consistently identifies uneven harm distributions, enabling policymakers and organizations to target their mitigation efforts effectively.","short_abstract":"The absolute dominance of Artificial Intelligence (AI) introduces unprecedented societal harms and risks. Existing AI risk assessment models focus on internal compliance, often neglecting diverse stakeholder perspectives and real-world consequences. We propose a paradigm shift to a human-centric, harm-severity adaptive...","url_abs":"https://arxiv.org/abs/2509.10104","url_pdf":"https://arxiv.org/pdf/2509.10104v1","authors":"[\"Sofia Vei\",\"Paolo Giudici\",\"Pavlos Sermpezis\",\"Athena Vakali\",\"Adelaide Emma Bernardelli\"]","published":"2025-09-12T09:52:45Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"stat.ME\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}