DRS: A Framework for Reproducible AI Decision-Making
Year: 2026
Type: article
Abstract: AI systems often exhibit inconsistent and non-reproducible behavior due to implicit assumptions, incomplete data usage, and uncontrolled reasoning processes. This lack of reproducibility limits auditability, trust, and practical deployment in critical domains. This paper introduces DRS (Data-Rules-Signals), a structural framework designed to enforce reproducibility in AI decision-making. DRS decomposes AI processes into three explicitly defined layers: Data (inputs), Rules (constraints), and Signals (derived features). By externalizing and enforcing these components, DRS enables consistent, auditable, and reproducible outputs across AI systems. The framework also provides a systematic way to identify failure modes, categorized into Data failure, Rules failure, and Signal failure. DRS offers a practical foundation for building reliable AI systems in domains where consistency and traceability are critical.
Author: Komuro, Takayasu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7140168024
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