Ethics of Using Data in Automated Decision-Making - Economicus

by Dokt. Geralda MUCEKU, Prof. Asoc. Dr. Mateo SPAHO, Prof. Dr. Hysen MUCEKU

(Comparative Perspectives on Transparency, Fairness, and Institutional Accountability in the Age of AI)

Abstract

This paper examines the ethical dimensions of data use in automated decision-making (ADM) systems and their implications for transparency, fairness, privacy, and accountability. As artificial intelligence (AI) and machine-learning technologies become increasingly integrated into governance and organizational decision processes, the boundaries between human and algorithmic agency are being redefined. The study seeks to analyze how ethical principles can be operationalized to ensure that data-driven automation supports, rather than undermines, human-centered governance.
Employing a qualitative, comparative, and interpretive methodology, the research synthesizes theoretical insights from authors such as Floridi (2021), Nissenbaum (2020), Vallor (2022), Eubanks (2018), and Crawford (2021) with institutional frameworks including the OECD Principles on AI (2025), UNESCO Recommendation on AI Ethics (2023), and the European Commission’s AI Act (2025). Empirical and policy analysis demonstrates that while global ethical standards converge around transparency, fairness, and accountability, their implementation remains uneven—particularly in transitional economies such as those of the Western Balkans.
The findings reveal that the ethical sustainability of ADM depends not only on legal and technical safeguards but also on institutional culture, moral responsibility, and cross-sectoral collaboration. Embedding ethics in automated decision systems enhances public trust, regulatory compliance, and long-term economic stability. The paper concludes that ethical governance should be treated as a structural component of digital transformation, ensuring that innovation and responsibility evolve in tandem.

Keywords: ethics, artificial intelligence, data governance, automated decision-making, transparency, fairness, accountability, privacy, governance.