Responsibility as an Error-Correcting Information Problem: Optimal Responsibility Codes, Legal Observability, and Verifiable Evidence in Autonomous Digital Systems
Year: 2026
Type: preprint
Abstract: Autonomous digital systems distribute consequential action across users, operators, models, agents, tools, and cloud services, yet accountability can fail before investigation because responsibility-relevant evidence may be erased, corrupted, or never preserved. We formulate responsibility as an error-correcting information problem. Legal observability quantifies responsibility-separating information; responsibility capacity measures its worst-case survival under an explicit adversary. We introduce responsibility distance, code rate, adaptive capacity, transferability, Byzantine witness resilience, and a mixed error-erasure theorem showing that unique decoding is guaranteed when 2t + b < d_R^min, where t is the corruption budget, b is the erasure budget, and d_R^min is the minimum responsibility distance. We further formulate minimum-cost responsibility coding as a binary multicover optimization that minimizes telemetry cost subject to a required recovery radius. TRACE-R combines abstaining reconstruction, structural-causal analysis, Ed25519 cross-domain receipts, replay protection, Merkle checkpoints, and strict source/derived/inferred separation. AutoResponsibilityBench contributes 16,000 principal method-trials plus coding, adaptive-attack, identifiability-interval, cryptographic, transferability, and identical-outcome experiments. Fano and Bhattacharyya bounds bracket empirical decoding difficulty and expose information loss independently of any particular reconstruction algorithm. TRACE-R achieves zero false attribution on deliberately non-identifiable paired cases, while forced baselines always answer. The framework turns accountability from retrospective explanation into responsibility-identifiable system design without conflating technical reconstruction with evidentiary admissibility or legal liability.
Author: Akhtar, Mohammad Amir Khusru
Language: English
Subfield: Law
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7204174929
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