arXiv:2605.20693v1 Announce Type: new Abstract: Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply. Existing discriminative representations often use anonymous embedding directions, while concept-bottleneck and LLM-assisted methods attach natural-language names to features without ensuring that those definitions are reproducible or distinct from the target label. We propose an operational criterion for interpretable discriminative text representations: each coordinate should satisfy conceptual
The proliferation of advanced AI models necessitates more robust interpretability, especially as these models are deployed in sensitive and critical applications.
This research directly addresses the 'black box' problem in AI, offering a path toward more trustworthy and accountable AI systems, crucial for regulatory acceptance and broader adoption.
The proposed operational criterion for interpretable discriminative text representations moves beyond anecdotal interpretability towards a more rigorous and auditable framework.
- · AI ethics and safety researchers
- · Regulatory bodies
- · AI developers focused on explainable AI
- · Industries requiring high-assurance AI
- · Developers deploying opaque AI models
- · Organizations relying solely on anonymous embedding methods
Improved trust and reliability in AI-driven decision-making systems using text representations.
Accelerated development and adoption of AI in highly regulated sectors due to enhanced auditability.
The establishment of new industry standards and certifications for AI interpretability, potentially shifting market demand towards explainable AI solutions.
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Read at arXiv cs.CL