arXiv:2606.12088v1 Announce Type: new Abstract: Most fairness research in NLP assumes direct access to protected attributes such as gender, race, or nationality. In practice, however, such information is often unavailable due to privacy constraints, missing metadata, or legal restrictions, even though models may infer it from indirect textual cues. This raises a key question: can debiasing succeed without direct access to sensitive attributes? We propose H-SAL, which performs post-hoc concept and attribute erasure using self-description text as an implicit debiasing signal. To support this set
The increasing prevalence of AI models in sensitive applications confronts the persistent challenge of bias, particularly when direct protected attribute data is unavailable.
This research addresses a critical gap in AI fairness, enabling mitigation of discrimination when explicit demographic information is legally or practically inaccessible, fostering more equitable AI deployment.
Current debiasing techniques often rely on explicit protected attributes; this method demonstrates effective debiasing without such direct access, enhancing real-world applicability.
- · NLP developers
- · Organizations with strict privacy regulations
- · Users of AI systems
- · Fairness researchers
- · Developers unable to adapt to new debiasing techniques
- · Systems with unmitigated implicit bias
AI systems will become more robust against implicit biases and discrimination, even in data-scarce or privacy-constrained environments.
This could lead to broader adoption of AI in sectors where privacy concerns previously hampered deployment, such as healthcare or finance.
The reduced implicit bias could build greater public trust in AI technologies, accelerating their integration into more aspects of society.
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