A Workflow for Full Traceability of AI Decisions

2025-11-14

AI Summary

High-risk AI automated decision-making systems suffer from opaque processes and ambiguous accountability, particularly lacking judicially admissible audit evidence. To address this, we propose an end-to-end traceable AI decision workflow that extends the Decision Bill of Materials (DBOM) framework across the full AI lifecycle—encompassing training, inference, and decision-making—and integrates confidential computing with trusted execution environments to ensure tamper-evident, cryptographically verifiable logging at every stage. The workflow enables complete component provenance, behavioral attribution, and regulatory compliance verification. We validate the prototype in a toxic mushroom classification use case, demonstrating its capacity to support legal accountability and regulatory auditing. This work establishes the first traceability paradigm for high-risk AI systems that simultaneously satisfies theoretical rigor and engineering feasibility, advancing responsible AI governance through auditable, forensically sound decision records.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Algorithmic accountability and transparency on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

An ever increasing number of high-stake decisions are made or assisted by automated systems employing brittle artificial intelligence technology. There is a substantial risk that some of these decision induce harm to people, by infringing their well-being or their fundamental human rights. The state-of-the-art in AI systems makes little effort with respect to appropriate documentation of the decision process. This obstructs the ability to trace what went into a decision, which in turn is a prerequisite to any attempt of reconstructing a responsibility chain. Specifically, such traceability is linked to a documentation that will stand up in court when determining the cause of some AI-based decision that inadvertently or intentionally violates the law. This paper takes a radical, yet practical, approach to this problem, by enforcing the documentation of each and every component that goes into the training or inference of an automated decision. As such, it presents the first running workflow supporting the generation of tamper-proof, verifiable and exhaustive traces of AI decisions. In doing so, we expand the DBOM concept into an effective running workflow leveraging confidential computing technology. We demonstrate the inner workings of the workflow in the development of an app to tell poisonous and edible mushrooms apart, meant as a playful example of high-stake decision support.

Problem

• Ensuring full traceability of AI decision processes
• Addressing lack of documentation in AI system decisions
• Providing verifiable evidence for AI decision accountability

Innovation

• Full traceability workflow for AI decisions
• Tamper-proof verifiable exhaustive decision traces
• DBOM expansion with confidential computing technology