Samenvatting

Algorithms that significantly impact individuals and society should be transparent, yet they can often function as complex black boxes. Such high-risk AI systems necessitate explainability of their inner workings and decision-making processes, which is also crucial for fostering trust, understanding, and adoption of AI. Explainability is a major topic, not only in literature (Maslej et al. 2024) but also in AI regulation. The EU AI Act imposes explainability requirements on providers and deployers of high-risk AI systems. Additionally, it grants the right to explanation for individuals affected by high-risk AI systems. However, legal literature illustrates a lack of clarity and consensus regarding the definition of explainability and the interpretation of the relevant obligations of the AI Act (See e.g. Bibal et al. 2021; Nannini 2024; Sovrano et al. 2022). The practical implementation also presents further challenges, calling for an interdisciplinary approach (Gyevnar, Ferguson, and Schafer 2023; Nahar et al. 2024, 2110). Explainability can be examined from various perspectives. One such perspective concerns a functional approach, where explanations serve specific functions (Hacker and Passoth 2022). Looking at this functional perspective of explanations, my previous work elaborates on the central functions of explanations interwoven in the AI Act. Through comparative research on the evolution of the explainability provisions in soft and hard law on AI from the High-Level Expert Group on AI, Council of Europe, and OECD, my previous research establishes that explanations in the AI Act primarily serve to provide understanding of the inner workings and output of an AI system, to enable contestation of a decision, to increase usability, and to achieve legal compliance (Van Beem, ongoing work, paper presented at Bileta 2025 conference; submission expected June 2025). Moreover, my previous work reveals that the AI lifecycle is an important concept in AI policy and legal documents. The AI lifecycle includes phases that lead to the design, development, and deployment of an AI system (Silva and Alahakoon 2022). The AI Act requires various explanations in each phase. The provider and deployer shall observe an explainability by design and development approach throughout the entire AI lifecycle, adapting explanations as their AI evolves equally. However, the practical side of balancing between clear, meaningful, legally compliant explanations and technical explanations proves challenging. Assessing this practical side, my current research is a case study in the agricultural sector, where AI plays an increasing role and where explainability is a necessary ingredient for adoption (EPRS 2023). The case study aims to map which legal issues AI providers, deployers, and other AI experts in field crop farming encounter. Secondly, the study explores the role of explainability (and the field of eXplainable AI) in overcoming such legal challenges. The study is conducted through further doctrinal research, case law analysis, and empirical research using interviews, integrating the legal and technical perspectives. Aiming to enhance trustworthiness and adoption of AI in agriculture, this research seeks to contribute to an interdisciplinary debate regarding the practical application of the AI Act's explainability obligations.