The future of auditing?

https://arxiv.org/abs/2510.26576

"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems

The increasing exploitation of Artificial Intelligence (AI) enabled systems in critical domains has made trustworthiness concerns a paramount showstopper, requiring verifiable accountability, often by regulation (e.g., the EU AI Act). Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the mechanisms used to achieve this. However, these methods are either expensive or heavily manual and ill-suited for the opaque, "black box" nature of most AI models. An intractable conflict emerges: high auditability and verifiability are required by law, but such transparency conflicts with the need to protect assets being audited-e.g., confidential data and proprietary models-leading to weakened accountability. To address this challenge, this paper introduces ZKMLOps, a novel MLOps verification framework that operationalizes Zero-Knowledge Proofs (ZKPs)-cryptographic protocols allowing a prover to convince a verifier that a statement is true without revealing additional information-within Machine-Learning Operations lifecycles. By integrating ZKPs with established software engineering patterns, ZKMLOps provides a modular and repeatable process for generating verifiable cryptographic proof of compliance. We evaluate the framework's practicality through a study of regulatory compliance in financial risk auditing and assess feasibility through an empirical evaluation of top ZKP protocols, analyzing performance trade-offs for ML models of increasing complexity.

Perspective.

https://sonoflaw.academy/index.php/slaj/article/view/3

Artificial Intelligence and Judicial Ethics: Balancing Efficiency, Accountability, and Human Judgment

This paper explores the ethical and legal challenges arising from the integration of Artificial Intelligence (AI) into judicial systems worldwide. While AI promises enhanced efficiency, consistency, and access to justice, its use in decision-making raises fundamental concerns about transparency, accountability, and moral responsibility. Drawing on legal philosophy, comparative case studies, and policy analysis, the paper proposes a normative framework for ethical AI in judicial contexts. It argues that human judgment must remain central to the exercise of justice, supported—but not replaced—by machine learning. Recommendations include establishing audit mechanisms, data transparency mandates, and ethics oversight bodies to safeguard judicial independence in the age of automation.

You know the defense lawyers are using it…

https://read.dukeupress.edu/fsr/article-abstract/37/3-4/241/403305/AI-in-ProsecutionBalancing-Innovation-with-Ethical

AI in Prosecution: Balancing Innovation with Ethical and Legal Responsibilities

The article explores sound approaches prosecutor offices can use in the adoption of AI technology. Prosecutors should consider various factors, including prosecutors’ legal and ethical duties, current use of AI by prosecutors, AI use by law enforcement agencies, and the future use of AI for prosecutors.