arXiv:2601.16398v3 Announce Type: replace-cross Abstract: Algorithmic audits are essential tools for examining systems for properties required by regulators or desired by operators. Current audits of large language models (LLMs) primarily rely on black-box evaluations that assess model behavior only through input-output testing. These methods are limited to tests constructed in the input space, often generated by heuristics. In addition, many socially relevant model properties (e.g., gender bias) are abstract and difficult to measure through text-based inputs alone. To address these limitation
The increasing deployment and societal impact of large language models necessitate more robust and transparent auditing methods beyond current black-box approaches.
This research introduces a novel white-box method for auditing LLMs, addressing critical limitations of current evaluation techniques and enabling deeper understanding of model behavior regarding abstract societal properties like bias.
The ability to perform white-box sensitivity auditing directly on LLM internals, using steering vectors, shifts auditing from superficial input-output tests to a more granular, interpretable, and effective analysis.
- · AI ethicists
- · Regulators
- · LLM developers
- · Users concerned with bias
- · Companies relying solely on black-box auditing
- · Opaque AI systems
Improved detection and mitigation of biases and undesirable behaviors in large language models.
Increased trust and adoption of AI systems due to enhanced transparency and accountability.
Potential for new regulatory frameworks explicitly requiring white-box audit capabilities for critical AI deployments.
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