TY - GEN
T1 - Trustworthy LLMs for Ethically Aligned AI-based Systems
T2 - International Conference on Software Business
AU - de Cerqueira, José Antonio Siqueira
AU - Rousi, Rebekah
AU - Xi, Nannan
AU - Hamari, Juho
AU - Kemell, Kai-Kristian
AU - Abrahamsson, Pekka
N1 - Publisher Copyright:
© 2024 Copyright for this paper by its authors.
PY - 2025
Y1 - 2025
N2 - In 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.
AB - In 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.
KW - AI ethics
KW - AI4SE
KW - Large Language Models
KW - Trustworthiness
UR - https://ceur-ws.org/Vol-3921/
M3 - Conference contribution
AN - SCOPUS:85218442051
T3 - CEUR Workshop Proceedings
BT - 15th International Conference on Software Business (PhD Retreat, Posters and Demos Track), ICSOB-C 2024
A2 - Deekshitha,
A2 - Santos, Rodrigo
A2 - Khanna, Dron
A2 - Elshan, Edona
PB - CEUR-WS
Y2 - 18 November 2024 through 20 November 2024
ER -