Towards responsible AI governance: a multidimensional ethical evaluation frameworkVisa övriga samt affilieringar
2026 (Engelska)Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases / [ed] Irena Koprinska; João Mendes-Moreira; Paula Branco, Cham: Springer, 2026, s. 70-85Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]
As Artificial Intelligence (AI) systems increasingly permeate sensitive domains such as finance, healthcare, and media, ensuring their ethical deployment has become a central concern for researchers, policymakers, and practitioners. Current auditing tools often assess isolated principles, such as fairness or explainability, lacking a comprehensive view of the ethical risks involved. This paper presents a multidimensional framework for ethical evaluation of AI systems, designed to support responsible AI governance and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed approach enables the simultaneous analysis of key ethical dimensions, including fairness, bias, explainability, robustness, transparency, and legal compliance. We demonstrate the applicability of this tool through one extensive case study: a credit scoring system, considered high-risk under the AI Act. This work contributes to operationalizing responsible AI governance, providing insight for policymakers, regulators, and practitioners to ensure ethical, legally compliant, and socially responsible AI deployment.
Ort, förlag, år, upplaga, sidor
Cham: Springer, 2026. s. 70-85
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 2839
Nyckelord [en]
AI Governance, Ethics, Evaluation, Sustainable Development Goals, Trustworthiness
Nationell ämneskategori
Etik Artificiell intelligens
Identifikatorer
URN: urn:nbn:se:umu:diva-254288DOI: 10.1007/978-3-032-19096-3_5Scopus ID: 2-s2.0-105040136573ISBN: 9783032190956 (tryckt)ISBN: 9783032190963 (digital)OAI: oai:DiVA.org:umu-254288DiVA, id: diva2:2076309
Konferens
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025, 15-19 September, 2025, Porto, Portugal
2026-06-222026-06-222026-06-22Bibliografiskt granskad