Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profiles
H-SAL uses self-description text for implicit debiasing, outperforming explicit-label debiasing.
Key Findings
Methodology
H-SAL performs debiasing using self-description text without relying on explicit protected attributes. It uses self-description text as an implicit debiasing signal, constructing a cross-domain fairness benchmark for helpfulness prediction.
Key Results
- On StackOverflow, H-SAL reduces TPR-Gap from 4.9% to 1.3%, outperforming explicit-label debiasing at 2.0%.
- In Mathematics, H-SAL reduces TPR-Gap from 4.8% to 1.6%.
- Experiments show H-SAL outperforms explicit-label debiasing in multiple technical domains.
Significance
This research expands representation-level fairness studies, providing a new benchmark for studying debiasing under realistic data constraints. It demonstrates that implicit signals can effectively debias without explicit protected attributes.
Technical Contribution
H-SAL offers a debiasing method without explicit protected attributes, showcasing how implicit text signals can be used effectively for debiasing. It provides new perspectives and methods for fairness research.
Novelty
This is the first to use implicit text signals for debiasing without explicit protected attributes. Compared to prior work, it does not rely on explicit labels, offering broader application possibilities.
Limitations
- H-SAL relies on the quality and informativeness of self-description text, which may be less effective when information is lacking.
- The method may not be as effective as explicit-label debiasing in certain domains.
Future Work
Future work can explore applying H-SAL in more domains and improving the debiasing effect of implicit signals.
AI Executive Summary
In the field of natural language processing, removing bias from models typically requires explicit protected attributes. However, this information is often missing in practical applications. H-SAL proposes a new debiasing method using self-description text as an implicit debiasing signal. Experiments show that H-SAL outperforms explicit-label debiasing in multiple domains, especially in technical fields. This research expands representation-level fairness studies, providing a new benchmark for studying debiasing under realistic data constraints.
Deep Analysis
Background
Fairness research in NLP often relies on explicit protected attributes like gender and race. However, these attributes are often missing in practical applications. Studies show models can infer this information from indirect textual cues.
Core Problem
The key question is how to perform debiasing without explicit protected attributes. Traditional methods rely on explicit labels, which are often missing in practice.
Innovation
H-SAL performs debiasing using self-description text without relying on explicit protected attributes. It uses implicit signals for debiasing, offering new perspectives and methods.
Methodology
- �� Use self-description text as an implicit debiasing signal
- �� Construct cross-domain fairness benchmark
- �� Compare effects of explicit-label debiasing and implicit signal debiasing
Experiments
Experiments are conducted across multiple domains, including StackOverflow, Mathematics, SuperUser, and English. Different models are compared, including BERT, Llama-3.1-8B, and Mistral-7B.
Results
Experiments show H-SAL outperforms explicit-label debiasing in multiple domains, especially in technical fields. It effectively reduces TPR-Gap while maintaining prediction accuracy.
Applications
The method can be used for NLP tasks requiring debiasing, especially when explicit protected attributes are lacking.
Limitations & Outlook
H-SAL relies on the quality and informativeness of self-description text, which may be less effective when information is lacking. Future research can explore improving the debiasing effect of implicit signals.
Plain Language Accessible to non-experts
Imagine you're in a large library searching for a book. Explicit labels are like the classification tags on the shelves, while implicit signals are like the book's cover and synopsis. H-SAL judges the book's category by reading its synopsis, without relying on the shelf's classification tags.
ELI14 Explained like you're 14
Imagine you're playing a game with many characters. Each character has their own story and background. H-SAL is like a smart player who judges their abilities by their stories, not by the game's explicit tags.
Glossary
Debiasing
Removing unfair biases from a model to ensure fairness across all groups.
Used in the paper to describe how biases are removed from models.
Implicit Signal
Information inferred from indirect textual cues.
Used for debiasing without relying on explicit labels.
Self-description Text
Textual information provided by users about themselves.
Used as an implicit debiasing signal.
TPR-Gap
Disparity in true positive rates across different groups.
Used to measure debiasing effectiveness.
H-SAL
A method using self-description text for debiasing.
The new method proposed in the paper.
Open Questions Unanswered questions from this research
- 1 How to improve debiasing effectiveness when information is lacking remains to be explored.
- 2 The effectiveness of implicit signals may vary across different domains.
Applications
Immediate Applications
NLP Tasks
Perform debiasing when explicit protected attributes are lacking, improving model fairness.
Long-term Vision
Cross-domain Application
Explore how to apply H-SAL in more domains, improving the debiasing effect of implicit signals.
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 setting, we introduce a multi-domain Stack Exchange-based fairness benchmark for helpfulness prediction that includes both explicit and implicit signals, enabling comparison between standard debiasing with protected labels and debiasing without access to sensitive information. Across encoder and decoder-only language models, we find that implicit self-description often matches or outperforms explicit-label-based debiasing. Our results broaden representation-level fairness research and provide a new benchmark for studying debiasing under realistic data constraints.