AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework

• URL: http://arxiv.org/abs/2509.10104v1
• Date: Fri, 12 Sep 2025 09:52:45 GMT
• Title: AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework
• Authors: Sofia Vei, Paolo Giudici, Pavlos Sermpezis, Athena Vakali, Adelaide Emma Bernardelli,
• Abstract summary: Existing AI risk assessment models focus on internal compliance, often neglecting diverse stakeholder perspectives and real-world consequences.<n>We propose a paradigm shift to a human-centric, harm-severity adaptive approach grounded in empirical incident data.<n>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.
• Score: 4.84912384919978
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm [39.043933213898136]
  Current AI safety frameworks, which often treat harmfulness as binary, lack the flexibility to handle borderline cases where humans disagree.<n>We introduce PluriHarms, a benchmark designed to study human harm judgments across two key dimensions -- the harm axis (benign to harmful) and the agreement axis (agreement to disagreement)<n>Our scalable framework generates prompts that capture diverse AI harms and human values while targeting cases with high disagreement rates, validated by human data.
  arXiv  Detail & Related papers  (2026-01-13T19:41:11Z)
• Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse [50.87630846876635]
  We develop nine detailed cyber risk models.<n>Each model decomposes attacks into steps using the MITRE ATT&CK framework.<n>Individual estimates are aggregated through Monte Carlo simulation.
  arXiv  Detail & Related papers  (2025-12-09T17:54:17Z)
• Adversarially-Aware Architecture Design for Robust Medical AI Systems [0.0]
  Adversarial attacks pose a severe risk to AI systems used in healthcare.<n>Our study explores these vulnerabilities through empirical experimentation on a dermatological dataset.<n>We conclude with a call for integrated technical, ethical, and policy-based approaches to build more resilient, equitable AI in healthcare.
  arXiv  Detail & Related papers  (2025-10-23T16:51:11Z)
• A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents [11.147381548503965]
  We construct a taxonomy specifically for Generative AI failures and map them to the harms they precipitate.<n>We report the prevalence of each type of harm, underlying failure mode, and harmed stakeholder, as well as their common co-occurrences.<n>Our work offers actionable insights to policymakers, developers, and Generative AI users.
  arXiv  Detail & Related papers  (2025-05-28T07:54:06Z)
• Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s) [0.0]
  Authors argue that ethical concerns about AI deployment vary significantly based on implementation context and specific use cases.<n>They propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing.
  arXiv  Detail & Related papers  (2025-01-20T19:38:21Z)
• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Control Risk for Potential Misuse of Artificial Intelligence in Science [85.91232985405554]
  We aim to raise awareness of the dangers of AI misuse in science. We highlight real-world examples of misuse in chemical science. We propose a system called SciGuard to control misuse risks for AI models in science.
  arXiv  Detail & Related papers  (2023-12-11T18:50:57Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework [0.9622882291833615]
  This paper proposes an alternative contextual, coherent, and commensurable (3C) framework for regulating artificial intelligence (AI) To ensure contextuality, the framework bifurcates the AI life cycle into two phases: learning and deployment for specific tasks, instead of defining foundation or general-purpose models. To ensure commensurability, the framework promotes the adoption of international standards for measuring and mitigating risks.
  arXiv  Detail & Related papers  (2023-03-20T15:23:40Z)
• Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions [46.87576410532481]
  We show that, despite their current huge success, deep learning based AI systems can be easily fooled by subtle adversarial noise. Based on a case study of skeleton-based human interactions, we propose a novel adversarial attack on interactions. Our study highlights potential risks in the interaction loop with AI and humans, which need to be carefully addressed when deploying AI systems in safety-critical applications.
  arXiv  Detail & Related papers  (2021-01-17T16:23:20Z)

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.