Addressing the Ethical Implications of AI Models Developed: A Case Study of Master's Degree Dissertations in Data Science for Industry and Society

Alina Călin

2025

Abstract

The increase in the development and use of AI models has generated many ethical and societal concerns. In this paper, we examine the ethical element in several dissertations presented in July and September 2024 by students enroled in the Data Science for Industry and Society Master’s Degree Programme. We assess the level of awareness of ethical principles by analysing in these case studies the ethical concerns addressed by most students, the ethical principles that are mostly neglected, and possible implications for the society. The findings reveal that data bias is the most addressed concern, while accountability is the most neglected ethical principle. Some recommendations for possible improvements include the use of ethical AI tools for the design and assessment of AI models and applications.

Paper Citation

in Harvard Style

Călin A. (2025). Addressing the Ethical Implications of AI Models Developed: A Case Study of Master's Degree Dissertations in Data Science for Industry and Society. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 279-286. DOI: 10.5220/0013112000003890

in Bibtex Style

@conference{icaart25,
author={Alina Călin},
title={Addressing the Ethical Implications of AI Models Developed: A Case Study of Master's Degree Dissertations in Data Science for Industry and Society},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013112000003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Addressing the Ethical Implications of AI Models Developed: A Case Study of Master's Degree Dissertations in Data Science for Industry and Society
SN - 978-989-758-737-5
AU - Călin A.
PY - 2025
SP - 279
EP - 286
DO - 10.5220/0013112000003890
PB - SciTePress