Label Forensics: Interpreting Hard Labels in Black-Box Text Classifier

Black-box text classifiers often operate without transparency: Many text classifiers deployed on the web have undocumented internal workings or intentionally hidden semantics, raising concerns about how they make decisions and whether they can be audited for bias or misuse.

A new method can reverse-engineer what labels actually mean: The paper introduces "label forensics," a technique that can infer the semantic meaning of a classifier's output labels by generating representative examples that reliably produce each label, helping investigators understand what a classifier has actually learned.

The approach balances precision and breadth: The method creates a distribution of example sentences for each label that maintains two critical properties—consistency (samples reliably get classified as that label) and coverage (examples span the full semantic range the classifier associates with that label)—ensuring the reconstructed meaning is both accurate and comprehensive.

This enables auditing of opaque AI systems: By reconstructing what labels mean in undocumented classifiers, this forensic approach allows researchers and regulators to audit whether systems are working as intended, detect unintended biases, or identify if classifiers have been repurposed for harmful uses.

Responsible AI requires interpretability tools: The work highlights why transparency mechanisms are essential for responsible AI deployment—when companies or developers won't document their systems, independent methods to understand classifier behavior become necessary safeguards against misuse.