Epistemic trustworthiness of AI: (the necessity of) explainable AI in legal decision-making
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14220%2F25%3A00142925" target="_blank" >RIV/00216224:14220/25:00142925 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.e-elgar.com/shop/gbp/research-handbook-on-epistemologies-of-law-9781035347995.html?srsltid=AfmBOoqAp-QxpWn5Lw8PNSIaGTsN_ZOKgPCv_24rDUPY70_wF2IK8eKA" target="_blank" >https://www.e-elgar.com/shop/gbp/research-handbook-on-epistemologies-of-law-9781035347995.html?srsltid=AfmBOoqAp-QxpWn5Lw8PNSIaGTsN_ZOKgPCv_24rDUPY70_wF2IK8eKA</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Epistemic trustworthiness of AI: (the necessity of) explainable AI in legal decision-making
Popis výsledku v původním jazyce
The integration of Artificial Intelligence (AI) in legal decision-making poses profound challenges to the epistemic trustworthiness and legitimacy of judicial processes. At the core of these challenges is the necessity for decisions to be transparent, comprehensible, and justified—standards fundamental to the right to a fair trial and the rule of law. This paper explores the tension between the opaque nature of advanced AI models and the established legal requirement for reasoned judgments. By examining case law under the European Convention on Human Rights (ECHR), it identifies the essential elements of judicial reasoning necessary to uphold the legitimacy of automated legal decisions. Explainable AI (xAI) emerges as a potential solution to the black-box problem inherent in many machine learning models. However, current xAI methodologies, such as model-specific or model-agnostic approaches, often fall short of providing explanations that are accessible and epistemically satisfying to diverse stakeholders. The paper highlights the critical need to adapt xAI outputs to meet the cognitive capacities of affected parties while preserving the efficiency and reliability of AI systems. Moreover, the study advocates for a hybrid approach, combining xAI techniques with domain-specific applications of Large Language Models (LLMs). This integration can enhance the interpretability and personalization of AI-driven legal decisions, enabling affected individuals to better understand the rationale behind judicial outcomes. By addressing the technical, epistemic, and normative dimensions of AI in judicial contexts, the chaoter underscores the importance of designing AI tools that do not merely optimize efficiency but also uphold the principles of fair trial central to the rule of law. Achieving this balance is imperative to ensure that AI's role in legal decision-making strengthens, rather than undermines, the legitimacy of the judiciary and the fundamental rights it is tasked to protect.
Název v anglickém jazyce
Epistemic trustworthiness of AI: (the necessity of) explainable AI in legal decision-making
Popis výsledku anglicky
The integration of Artificial Intelligence (AI) in legal decision-making poses profound challenges to the epistemic trustworthiness and legitimacy of judicial processes. At the core of these challenges is the necessity for decisions to be transparent, comprehensible, and justified—standards fundamental to the right to a fair trial and the rule of law. This paper explores the tension between the opaque nature of advanced AI models and the established legal requirement for reasoned judgments. By examining case law under the European Convention on Human Rights (ECHR), it identifies the essential elements of judicial reasoning necessary to uphold the legitimacy of automated legal decisions. Explainable AI (xAI) emerges as a potential solution to the black-box problem inherent in many machine learning models. However, current xAI methodologies, such as model-specific or model-agnostic approaches, often fall short of providing explanations that are accessible and epistemically satisfying to diverse stakeholders. The paper highlights the critical need to adapt xAI outputs to meet the cognitive capacities of affected parties while preserving the efficiency and reliability of AI systems. Moreover, the study advocates for a hybrid approach, combining xAI techniques with domain-specific applications of Large Language Models (LLMs). This integration can enhance the interpretability and personalization of AI-driven legal decisions, enabling affected individuals to better understand the rationale behind judicial outcomes. By addressing the technical, epistemic, and normative dimensions of AI in judicial contexts, the chaoter underscores the importance of designing AI tools that do not merely optimize efficiency but also uphold the principles of fair trial central to the rule of law. Achieving this balance is imperative to ensure that AI's role in legal decision-making strengthens, rather than undermines, the legitimacy of the judiciary and the fundamental rights it is tasked to protect.
Klasifikace
Druh
C - Kapitola v odborné knize
CEP obor
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OECD FORD obor
50501 - Law
Návaznosti výsledku
Projekt
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Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název knihy nebo sborníku
Research Handbook on Epistemologies of Law
ISBN
9781035347995
Počet stran výsledku
13
Strana od-do
355-367
Počet stran knihy
456
Název nakladatele
Edward Elgar Publishing
Místo vydání
Cheltenham
Kód UT WoS kapitoly
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