From Black Box to Open Court: Solving the Interpretability Paradox in AI-Assisted Justice

The increasing integration of artificial intelligence (AI) into legal systems, especially in the form of AI-assisted legal reasoning, raises significant concerns about transparency, accountability, and trust. The primary question this research seeks to answer is: how does the "Interpretability Paradox" in AI-assisted legal reasoning impact the acceptance and application of AI tools in judicial decision-making? This paradox refers to the tension between the need for AI systems to be interpretable—so that their decision-making processes are understandable to human users—and the complexity of sophisticated AI models, which often operate as "black boxes." This study examines the implications of this tension, particularly in legal settings where transparency, justification, and trust in decision-making are paramount. Current literature on AI in justice often focuses on the benefits of digital technologies in enhancing efficiency and consistency, such as automated decision-making and predictive analytics in courtrooms. However, there is a notable gap regarding how the interpretability of AI models—especially in high-stakes environments like the justice system—affects the willingness of judges and lawyers to rely on AI tools. Scholars have acknowledged the challenges of AI, but the specific impact on legal practices remains underexplored. By filling this gap, this study expands on existing theories of trust and transparency in AI and legal systems. This research employs a desktop-based quantitative methodology, analysing key case studies of AI implementation in judicial contexts, legal frameworks, and technical documents outlining AI tools used in court systems. It utilizes theoretical frameworks from AI ethics, legal theory, and technology governance to evaluate the extent to which the interpretability paradox is addressed in existing AI systems and legal policies. Preliminary findings suggest that the interpretability paradox poses significant ethical and practical challenges. Judges and lawyers’ express concerns about AI recommendations when the decision-making process cannot be fully explained or justified. Furthermore, a lack of interpretability undermines the legal principle of "reasoned decisions," which is essential for both judicial accountability and the public's trust in the legal system. This study concludes that overcoming the interpretability paradox is crucial for ensuring the ethical integration of AI into legal practices, as it impacts both the effectiveness of AI tools and the public’s perception of AI-driven justice. This research contributes to the ongoing discourse on the digitalization of justice, highlighting the challenges AI poses to legal reasoning and providing recommendations for improving transparency in AI systems used in courts. Its findings are relevant to scholars, policymakers, legal practitioners, and AI developers who aim to create more accountable, transparent, and trustworthy AI tools for the justice sector. By addressing the interpretability paradox, this research advances the conversation on the responsible use of AI in courts, contributing to a more equitable and effective justice system.