A Conceptual View on the Design and Properties of Explainable AI Systems for Legal Settings

Otterlo, Martijn van; Atzmueller, Martin

doi:10.1007/978-3-030-89811-3_10

Cited by 1 publication

(1 citation statement)

References 59 publications

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“…Indeed, the ethical implications of AI technologies have gained significant attention due to their potential to perpetuate existing inequalities, produce unintended negative consequences, and create new ethical dilemmas Buyl et al, 2022;Jobin et al, 2019;Pastaltzidis et al, 2022). However, the extent to which XAI research genuinely addresses ethical considerations, and effectively assimilates them into the design, development, and evaluation of AI systems, remains a topic of considerable debate (Balasubramaniam et al, 2023;van Otterlo & Atzmueller, 2020;Kaur et al, 2020;Alufaisan et al, 2021;Chazette et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Mapping the landscape of ethical considerations in explainable AI research

Nannini,

Marchiori Manerba,

Beretta

2024

Ethics Inf Technol

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With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research frequently asserts its relevance to ethical considerations. Yet, the substantiation of these claims with rigorous ethical analysis and reflection remains largely unexamined. This contribution endeavors to scrutinize the relationship between XAI and ethical considerations. By systematically reviewing research papers mentioning ethical terms in XAI frameworks and tools, we investigate the extent and depth of ethical discussions in scholarly research. We observe a limited and often superficial engagement with ethical theories, with a tendency to acknowledge the importance of ethics, yet treating it as a monolithic and not contextualized concept. Our findings suggest a pressing need for a more nuanced and comprehensive integration of ethics in XAI research and practice. To support this, we propose to critically reconsider transparency and explainability in regards to ethical considerations during XAI systems design while accounting for ethical complexity in practice. As future research directions, we point to the promotion of interdisciplinary collaborations and education, also for underrepresented ethical perspectives. Such ethical grounding can guide the design of ethically robust XAI systems, aligning technical advancements with ethical considerations.

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