Assessing trustworthy AI: Technical and legal perspectives of fairness in AI

Abstract

Artificial Intelligence systems are used more and more nowadays, from the application of decision support systems to autonomous vehicles. Hence, the widespread use of AI systems in various fields raises concerns about their potential impact on human safety and autonomy, especially regarding fair decision-making. In our research, we primarily concentrate on aspects of non-discrimination, encompassing both group and individual fairness. Therefore, it must be ensured that decisions made by such systems are fair and unbiased. Although there are many different methods for bias mitigation, few of them meet existing legal requirements. Unclear legal frameworks further worsen this problem. To address this issue, this paper investigates current state-of-the-art methods for bias mitigation and contrasts them with the legal requirements, with the scope limited to the European Union and with a particular focus on the AI Act. Moreover, the paper initially examines state-of-the-art approaches to ensure AI fairness, and subsequently, outlines various fairness measures. Challenges of defining fairness and the need for a comprehensive legal methodology to address fairness in AI systems are discussed. The paper contributes to the ongoing discussion on fairness in AI and highlights the importance of meeting legal requirements to ensure fairness and non-discrimination for all data subjects.