Explainability gaps in AI-driven criminal justice governance: the RisCanvi case

Abstract The deployment of artificial intelligence (AI) in criminal justice is no longer experimental but routine. In Catalonia, this shift is embodied by RisCanvi, a risk assessment system used to guide decisions on imprisonment, conditional release, and rehabilitation. Initially built on expert-defined rules to standardize classifications and reduce bias, RisCanvi has gradually evolved into an opaque, data-driven infrastructure at the core of penal governance. A key turning point came in 2019, when the system moved from a transparent weighted-sum model to a logistic regression embedded in the prison administration’s digital architecture. While this change sought better predictive performance, it also concealed variables, weights, and thresholds from most professionals and all affected individuals. As a result, those whose liberty is at stake cannot meaningfully understand, question, or contest the algorithmic logic that shapes their trajectories. This paper proposes a governance and explainability framework for high-risk AI systems like RisCanvi. Building on the Trustworthy AI paradigm, and aligning with the EU AI Act and ISO/IEC 42,001, we introduce a three-layer model of intelligibility: algorithmic explainability, human-centred interface design, and stakeholder-informed narrative communication. The framework aims to enhance auditability, support procedural fairness, and restore agency and accountability in AI-assisted criminal justice decisions.