Trustworthy LLMs for Ethically Aligned AI-based Systems: A PhD Research Plan

dc.contributor.authorde Cerqueira, José Antonio Siqueira
dc.contributor.authorRousi, Rebekah
dc.contributor.authorXi, Nannan
dc.contributor.authorHamari, Juho
dc.contributor.authorKemell, Kai Kristian
dc.contributor.authorAbrahamsson, Pekka
dc.contributor.editorM., Deekshitha
dc.contributor.editorSantos, Rodrigo
dc.contributor.editorKhanna, Dron
dc.contributor.editorElshan, Edona
dc.contributor.orcidhttps://orcid.org/0000-0001-5771-3528
dc.date.accessioned2026-01-26T15:53:11Z
dc.date.issued2025
dc.description.abstractIn response to growing concerns around trustworthiness and ethical alignment in AI systems, this PhD aims to investigate how Large Language Models (LLMs) can be leveraged to support ethically aligned AI development in software engineering. Despite advancements, integrating ethical principles into AI workflows remains challenging, particularly in real-world applications that require compliance with emerging regulations, such as the EU AI Act. We will develop a Visual Studio Code (VSCode) Generative AI (GenAI) Extension powered by a multi-agent LLM system with Retrieval-Augmented Generation (RAG) capabilities. The extension will be designed to aid developers by evaluating code compliance with ethical standards, providing actionable recommendations to embed trustworthiness from early stages of development. The GenAI Extension will be evaluated through an iterative design science approach, encompassing dataset generation, ethical benchmarking, and practitioner testing. A dataset of over 2000 ethically aligned AI systems, will be created in compliance with leading regulatory frameworks, serving as a foundation for this tool’s assessments. With this work, we hope to assist developers, particularly in startups and SMEs, by providing practical resources for building ethically aligned AI within limited resources. Through this approach, we aim to bridge the gap between abstract ethical principles and actionable software development practices, making ethical AI more accessible across industry contexts.en
dc.description.notification© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
dc.description.reviewstatusfi=vertaisarvioitu|en=peerReviewed|
dc.identifier.urihttps://osuva.uwasa.fi/handle/11111/19691
dc.identifier.urnURN:NBN:fi-fe202601269095
dc.language.isoen
dc.publisherRWTH Aachen
dc.relation.conferenceInternational Conference on Software Business (PhD Retreat, Posters & Demos Track)
dc.relation.funderJane ja Aatos Erkon säätiöfi
dc.relation.funderJane ja Aatos Erkko Foundationen
dc.relation.grantnumber220025
dc.relation.ispartofICSOB-C 2024 Software Business: PhD Retreat and Posters & Demos Track 2024
dc.relation.ispartofjournalCEUR workshop proceedings
dc.relation.issn1613-0073
dc.relation.urlhttps://ceur-ws.org/Vol-3921/phd-paper1.pdf
dc.relation.urlhttps://urn.fi/URN:NBN:fi-fe202601269095
dc.relation.volume3921
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.source.identifier2-s2.0-85218442051
dc.source.identifiera1a61043-df66-432e-8e20-dcfff00a781d
dc.source.metadataSoleCRIS
dc.subjectAI ethics
dc.subjectAI4SE
dc.subjectLarge Language Models
dc.subjectTrustworthiness
dc.subject.disciplinefi=Viestintätieteet|en=Communication Studies|
dc.titleTrustworthy LLMs for Ethically Aligned AI-based Systems: A PhD Research Plan
dc.type.okmfi=A4 Vertaisarvioitu artikkeli konferenssijulkaisussa|en=A4 Article in conference proceedings (peer-reviewed)|
dc.type.publicationarticle
dc.type.versionpublishedVersion

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