2026-08-11T23:13:32Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:22747 2025-10-02T11:25:48Z ddc:004 open_access

Towards Trusted AI: A Blueprint for Ethics Assessment in Practice (Academic Track) Wirth, Christoph Tobias Maftei, Mihai Martín-Peña, Rosa Esther Merget, Iris Trusted AI Trustworthy AI AI Ethics Assessment Framework AI Quality AI Ethics AI Ethics Assessment AI Lifecycle Responsible AI Ethics-By-Design AI Risk Management Ethics Impact Assessment AI Ethics KPIs Human-Centric AI Applied Ethics The development of AI technologies leaves place for unforeseen ethical challenges. Issues such as bias, lack of transparency and data privacy must be addressed during the design, development, and the deployment stages throughout the lifecycle of AI systems to mitigate their impact on users. Consequently, ensuring that such systems are responsibly built has become a priority for researchers and developers from both public and private sector. As a proposed solution, this paper presents a blueprint for AI ethics assessment. The blueprint provides for AI use cases an adaptable approach which is agnostic to ethics guidelines, regulatory environments, business models, and industry sectors. The blueprint offers an outcomes library of key performance indicators (KPIs) which are guided by a mapping of ethics framework measures to processes and phases defined by the blueprint. The main objectives of the blueprint are to provide an operationalizable process for the responsible development of ethical AI systems, and to enhance public trust needed for broad adoption of trusted AI solutions. In an initial pilot the blueprinted for AI ethics assessment is applied to a use case of generative AI in education. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Christoph Tobias Wirth and Mihai Maftei and Rosa Esther Martín-Peña and Iris Merget 2025 Is Part Of OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.SAIA.2024.7 urn:nbn:de:0030-drops-227478 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.7 eng https://creativecommons.org/licenses/by/4.0/legalcode