Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profiles

Problem
This work addresses the gap in fairness research in natural language processing (NLP) that typically relies on direct access to protected attributes (e.g., gender, race) for debiasing. The authors highlight that in real-world applications, such attributes may be unavailable due to privacy concerns, missing metadata, or legal restrictions. This limitation raises the question of whether effective debiasing can be achieved without direct access to sensitive information. The paper is a preprint and has not undergone peer review.

Method
The authors propose H-SAL (Hidden-Sensitive Attribute Learning), a post-hoc debiasing method that utilizes self-description text as an implicit signal for concept and attribute erasure. H-SAL operates on a multi-domain Stack Exchange-based fairness benchmark designed for helpfulness prediction, which includes both explicit and implicit signals. The architecture leverages encoder and decoder-only language models, although specific model types are not disclosed. The training process involves comparing standard debiasing techniques that utilize protected labels against those that rely solely on implicit self-description signals. The authors employ a variety of metrics to evaluate the effectiveness of their approach.

Results
The experimental results demonstrate that H-SAL achieves comparable or superior performance in debiasing compared to traditional methods that use explicit protected attributes. Specifically, the authors report that implicit self-description signals can match or outperform explicit-label-based debiasing across various encoder and decoder models. The paper provides quantitative results on the new benchmark, although specific headline numbers and effect sizes are not detailed in the summary.

Limitations
The authors acknowledge that their approach may not generalize across all domains or datasets, as the effectiveness of implicit signals can vary based on the context and the nature of the self-description text. Additionally, the reliance on self-description may introduce its own biases, which are not fully explored in this work. The authors also note that the benchmark created may not encompass all potential scenarios for debiasing, limiting its applicability.

Why it matters
This research has significant implications for advancing fairness in NLP, particularly in scenarios where sensitive attributes are not accessible. By demonstrating that debiasing can be effectively achieved through implicit signals, the work opens new avenues for developing fairer models in compliance with privacy regulations. The introduction of a new benchmark for studying debiasing under realistic constraints further enriches the field, encouraging future research to explore alternative debiasing strategies. This work contributes to the ongoing discourse on representation-level fairness in NLP, as published in arXiv cs.CL.