Telemetry Data as Digital Testimony: A New Paradigm for Forensic Verification and Liability Attribution of AI Systems

After AI is deeply integrated into social operations, liability determination faces black-box challenges that traditional frameworks are difficult to address. This article proposes using telemetry data as digital testimony to construct a forensic framework for systemic awareness and causal liability, providing a new paradigm for AI system liability attribution and helping to clarify the legal responsibilities of human liable parties.

In 2026, AI will be deeply integrated into society. Traditional legal liability frameworks, which are based on human actors, struggle to deal with the black-box nature of AI's complex nonlinear decision-making. Drawing on the principle of aviation black boxes, the study proposes telemetry data as a solution to record the entire decision-making process of the system to provide objective technical evidence.

Telemetry data is similar to software logs but more professional, including: Internal state information: Neural network activation patterns, attention weight distribution, etc.; Decision path tracking: Factors considered during decision-making, excluded options, and trade-off logic; Abnormal event records: Abnormal inputs, calculation errors, etc.; Resource usage statistics: CPU/GPU resource status, etc.; External dependency status: Interaction status with databases/APIs.

Systemic awareness: Beyond individual subjectivity, it includes architecture awareness (built-in risk cognition in design), data awareness (inheritance of biases in training data), optimization awareness (value orientation of objective functions), and operation awareness (self-monitoring mechanisms); Causal liability reconstruction: Adopts counterfactual analysis (simulating the impact of parameter changes), attribution analysis (quantifying feature contributions via integrated gradients/SHAP values), intervention analysis (evaluating the effect of safety constraints), and path analysis (tracking information flow in neural networks).

Telemetry data meets the core functions of testimony: Objectivity: Real-time recording is not affected by human memory; Completeness: Captures massive technical details; Verifiability: Cross-verifiable with technical documents; Traceability: Reconstructs the decision timeline; It faces challenges such as tampering and incompleteness, which need to be addressed through a credibility assessment framework (integrity verification, source authentication, technical audit).

Applicable to:

• Autonomous driving accident investigation (revealing system status and decisions);
• Medical AI liability determination (distinguishing between algorithmic defects/data issues);
• Financial AI regulatory compliance (verifying transaction system compliance);
• Product liability litigation (key evidence);
• Criminal investigation (providing clues).

It faces challenges such as technical complexity (difficult for judges to understand), privacy conflicts, lack of standardization, adversarial manipulation, and legal lag; telemetry data is an AI accountability mechanism that helps clarify human responsibilities, promotes the integration of technology and law, ensures AI safety and controllability, and becomes increasingly important as AI complexity increases.