Digital Forensics of AI Systems: Forensic Application of Telemetry Data in Liability Attribution

In 2026, as AI technology deeply permeates society, digital forensics of AI systems has become an emerging forensic branch. When AI makes wrong decisions, exhibits algorithmic bias, or causes harm, traditional forensic methods are limited. Telemetry data, as the "digital footprint" of AI, records the entire decision-making chain and provides technical transparency for liability attribution. This article explores the technical foundation, legal challenges, and practical applications of telemetry data, and analyzes its role as a "digital testimony" in AI liability determination.

With the popularization of AI technology, problems such as wrong decisions and algorithmic bias in complex machine learning models occur frequently, and traditional forensic methods are difficult to deal with. Different from traditional logs, telemetry data contains in-depth information such as model internal states and decision paths. It is a key clue to uncover the truth of AI decisions and has become a new frontier in forensic science.

AI telemetry data contains multi-layer information:

• Input Layer: Records the receiving time, format, source, etc., of input data to verify input integrity;
• Preprocessing Layer: Records data cleaning, feature extraction, etc., to identify preprocessing biases;
• Model Internal State: Neural network activation values, attention weights, etc., to reveal the internal "thinking process";
• Decision Path: Records decision factors, excluded options and reasons to understand the reasoning process;
• Output Layer: Output generation process, confidence level, etc., to evaluate reliability;
• System Environment: Hardware, network status, etc., to analyze environmental impacts.

As electronic evidence, telemetry data needs to address legal issues:

• Evidentiary Qualification: Must meet authenticity, legality, and relevance, and establish a complete evidence chain to prevent tampering;
• Technical Reliability: Verify the accuracy and anti-tampering ability of the telemetry system through audits;
• Expert Witness: Qualified AI forensics experts are needed to explain the meaning of the data;
• Privacy Balance: Balance forensics and privacy protection through technologies such as desensitization and selective disclosure.

Causal relationship analysis in AI liability determination requires a new framework:

• Counterfactual Reasoning: Simulate parameter/input changes to evaluate result differences;
• Attribution Analysis: Quantify feature contributions using integrated gradients, SHAP values, etc.;
• Path Analysis: Track the information flow chain of neural networks;
• Intervention Analysis: Intervene parameters and observe output changes to determine causal strength. Systemic liability is decomposed into design, data, deployment, and supervision liabilities, and telemetry data helps identify liabilities at all levels.

Hypothetical case: An AI diagnostic system in a hospital misjudged a benign tumor as malignant, leading to unnecessary chemotherapy for the patient. Telemetry analysis:

• Input layer: CT image quality is good and complete;
• Preprocessing layer: Image standardization is normal;
• Model internal: Abnormal activation when identifying a specific texture, which is strongly correlated with malignancy in the training data;
• Decision path: Ignored other indicators based on this texture;
• Conclusion: Sampling bias in training data caused the error, and the liability lies in the data collection and preprocessing stage.

Technical challenges:

• Data explosion: Need efficient storage and retrieval;
• Real-time requirements: High computing performance needs;
• Interpretability limitations: Need better visualization tools;
• Adversarial attacks: Prevent malicious interference;
• Privacy protection: Support from technologies such as differential privacy. Standardization measures: Unified data format, record integrity standards, time synchronization, encryption signatures, and privacy protection standards.

Future trends: Automated analysis tools, blockchain integration to ensure data tamper-proofing, quantum security protection, cross-system forensics frameworks, and real-time monitoring and prevention. Conclusion: AI telemetry forensics is a frontier of the integration of technology and law. It realizes AI accountability to maintain justice and promote the healthy development of technology. We need to balance technological innovation and social responsibility to ensure that AI serves human well-being.