Epistemic Blinding: An Interpretability Protocol for Auditing Prior Contamination in LLM Analysis

This article proposes the 'Epistemic Blinding' inference-time protocol, aiming to identify and quantify the problem of LLMs mixing data-driven reasoning with training-memory priors in analytical tasks. By anonymizing entity identifiers and comparing blinded vs. non-blinded results, it restores auditable dimensions and helps distinguish the source of outputs. Validated through experiments in drug target discovery and stock screening, it provides open-source tools and Claude Code skills to lower the application barrier, serving as a key infrastructure for rebuilding AI trust.

When LLMs are applied to scientific/business analysis, there is a trust crisis—outputs may mix input data and training-memory priors, and a single output cannot distinguish the source. This 'epistemic contamination' is particularly prominent in high-risk fields like drug target discovery (e.g., models are affected by gene 'reputation' bias). Traditional evaluations focus on accuracy but ignore auditability, while scientific/financial scenarios need to know 'why the conclusion is this way'—the current limitation of non-interpretable LLM outputs urgently needs to be addressed.

The core idea is to replace entity identifiers (e.g., gene/company names) with anonymous codes before input, and compare with the non-blinded group. Design advantages: 1. Isolate variables, eliminate the model's prior access to the entity's 'reputation', and force it to rely on input data; 2. Quantify and compare differences to measure the degree of prior contamination; 3. Maintain practicality, restore audit dimensions (the ratio of input data to parameterized knowledge) instead of pursuing certainty.

1. Tumor drug target discovery: In tasks involving four cancer types, blinding led to a 16% change in Top20 results (indicating prior influence), without impairing the ability to identify validated targets; 2. S&P500 stock screening: Brand perception bias caused the Top20 ranking to change by 30-40% under different random seeds, revealing that AI investment analysis may have systemic biases that traditional backtesting cannot detect.

The research team provides open-source tools that can be integrated into existing LLM workflows; they also launched Claude Code skills to support 'one-click blinding', significantly lowering the adoption barrier. It is of great value for scenarios requiring high auditability, such as regulatory reporting, scientific discovery, and medical diagnosis.

Limitations: The goal is not to produce better results, but to audit whether the model follows the analytical process (reasonable priors are sometimes beneficial). Outlook: 1. Dynamic blinding strategy (adaptively selecting entities to blind); 2. Hierarchical audit mechanism (a continuous spectrum from full blinding to transparency); 3. Cross-modal expansion (applied to multi-modal scenarios like vision-language).

Epistemic Blinding marks a shift in LLM evaluation from 'performance metrics' to 'process auditability'. In the context of AI participating in high-risk decisions, distinguishing between data-driven conclusions and memory biases is the infrastructure for building human-AI trust. For practitioners in science, finance, and healthcare, it provides a practical method to verify whether AI analysis meets rigorous standards, representing an important progress in interpretable and trustworthy AI.