Abstract
Presented herein is a system for preventing anchoring bias in large language model (LLM) investigative analysis. Bias in LLMs is not typically addressed by instruction-level approaches; rather, it is most effectively mitigated by physically separating the biasing data from an LLM's input during the hypothesis formation phase. The proposed system includes five core components: a Data Redaction Engine, a Staged Analysis Protocol, a Structural Gate, a Corrections Bridge, and a Procedural Attestation Record. The Data Redaction Engine operates to separate source data into a redacted evidence set and an attribution set, validated by a multi-layer validation pass. The Staged Analysis Protocol enforces a strict temporal ordering: blind analysis first (evidence only), then verification (evidence + attribution). The Structural Gate prevents stage progression until a structured hypothesis artifact is created, validated, and persisted. The Corrections Bridge captures discrepancies between the blind hypothesis and verified findings without modifying the original hypothesis, creating a complete audit trail. Finally, a Procedural Attestation Record documents that the protocol was followed correctly, with optional cryptographic hash enhancement.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Chhabra, Avneet Singh, "TECHNIQUES FOR PREVENTING ANCHORING BIAS IN AI INVESTIGATIVE ANALYSIS THROUGH STAGED DATA REDACTION AND HYPOTHESIS-GATED REVELATION", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10414