🤖 AI Summary
This study addresses the limitations of existing explainable artificial intelligence (XAI) approaches in legal contexts such as credit scoring, which prioritize technical interpretability while neglecting legal justifiability and thus inadequately safeguard creditors’ rights. To overcome this gap, the paper proposes a novel paradigm—“justifiable AI”—that embeds legitimacy requirements from the European Union’s legal framework directly into the core of AI decision-making. By integrating legal text analysis, model explanation techniques, and compliance assessment, this approach constructs a coherent argumentation system that harmonizes legal and technical reasoning. The proposed method transcends conventional XAI constraints, offering a viable solution for high-risk AI applications that simultaneously ensures legal validity and technical feasibility, thereby substantively strengthening the protection of creditors’ rights.
📝 Abstract
Artificial intelligence-based solutions offer new efficiency-increasing possibilities in many applications, including credit scoring. Yet, the increasing sophistication of machine-learning models in use raises concerns regarding many of their aspects, explainability notwithstanding. We review the relevant EU legal background and integrate this review with insights from technical sciences to interpret relevant legal provisions in the light of technological possibilities. We reject the narrow interpretations of the right to explanation and suggest the broad one, which encompasses not only a technical explanations but also a legal justification as the only one that allows to safeguard the creditors rights in an operative manner.