Open-DeBias: Efficient Open-Set Bias Mitigation

• Open-DeBias is a framework that efficiently mitigates open-set social and stereotypical biases in question answering by leveraging lightweight adapter modules.
• It employs OpenBiasBench, a comprehensive benchmark covering diverse bias categories and contexts, to rigorously test and improve debiasing performance.
• The method demonstrates strong cross-lingual and zero-shot generalization, outperforming prior debiasing approaches in both accuracy and fairness.

Open-DeBias is a data- and parameter-efficient framework for mitigating open-set social and stereotypical biases in LLMs, specifically targeting question answering (QA) tasks where both known and previously unseen biases may emerge. The methodology is distinguished by its use of adapter modules, a novel open-set bias benchmark (OpenBiasBench), and a loss function that accommodates ambiguous and disambiguated evaluation contexts. Open-DeBias demonstrates robust generalization across a wide spectrum of bias categories, strong cross-lingual transfer, and superior QA performance compared to prior state-of-the-art debiasing methods (Rani et al., 28 Sep 2025).

1. Motivation and Problem Definition

Most existing bias mitigation techniques in LLMs operate in a closed-set paradigm: they assume a fixed list of bias categories (e.g., gender, race) and require explicit supervision over these. However, real-world QA deployments increasingly confront emergent, context-specific, or dynamic biases outside of any predefined taxonomy. Open-DeBias specifically addresses this "open-set bias" problem by enabling debiasing not only for known social or stereotypical biases but also for novel, previously unseen subgroups, improving generalization and fairness in model predictions.

The framework is motivated by two key shortcomings in existing practice:

• State-of-the-art bias mitigation is largely category-constrained and does not generalize to new or emergent forms of bias in QA.
• Reliance on comprehensive bias annotations is costly and infeasible at scale; thus, scalable, domain-agnostic, and efficient debiasing methods are needed.

2. OpenBiasBench: A Comprehensive Open-Set Bias Benchmark

To support robust evaluation of open-set bias mitigation, the authors develop OpenBiasBench, an extensive multiple-choice QA dataset constructed by:

• Extracting diverse image captions from MS COCO.
• Prompting Gemini-1.5-Flash to identify candidate bias concepts, categories, and classes, and to generate QA pairs controlling for bias visibility/ambiguity.

OpenBiasBench covers 473,000+ instances across 31 high-level bias categories and nearly 10,000 fine-grained subgroups. Categories span not only protected attributes (e.g., race, gender, religion) but also brand, location, aesthetics, disability, myth, and more.

Significance:

• Provides a rigorous, large-scale stress-test for models’ ability to detect and mitigate both explicit and previously unseen forms of bias.
• Exposes models to ambiguous and disambiguated contexts, enabling examination of both overt and subtle societal stereotyping.

3. Methodology: Adapter-Based Open-Set Debiasing

Open-DeBias employs lightweight adapter modules within a transformer-based LLM to achieve efficient, modular bias mitigation. The methodology comprises several components:

• Adapter Module Placement: Adapters are inserted before and after the feed-forward network (FFN) blocks in each transformer layer. These modules are responsible for learning bias-specific features without modifying the frozen backbone model weights.
• Parameter- and Data-Efficiency: The adapters are fine-tuned only on a small subset of representative bias categories, yet are expected to generalize to unseen biases (open-set transfer). Only the adapter and fusion layer parameters are updated, maintaining computational and data efficiency.
• Fusion Layer: When multiple adapters are used (potentially for separate bias categories or linguistic domains), a fusion layer combines their outputs. This mechanism facilitates unified debiasing even when multiple bias domains are encountered.
• Loss Function: The training objective is a combination of cross-entropy and Kullback-Leibler divergence losses:

$$\mathcal{L} = \mathcal{L}_\text{CE} + \lambda \cdot \mathcal{L}_\text{KL}$$

• For disambiguated cases (where the bias context is explicit), standard cross-entropy loss $\mathcal{L}_\text{CE}$ is used to maximize the correctness of the answer.
• For ambiguous cases, $\mathcal{L}_\text{KL}$ regularizes model predictions to a uniform distribution across non-neutral choices, capturing the "unknown" answer and ensuring ambiguity-resilient debiasing.
  • QA Prediction Objective: Given a context $ctx$ and question $q$, select the best answer $a^*$ from candidate set $\mathcal{A}$ via

$$a^* = \arg\max_{a_i \in \mathcal{A}} p(a_i \mid ctx, q).$$

4. Experimental Evaluation and Results

Open-DeBias is benchmarked against state-of-the-art methods such as BMBI on multiple datasets: OpenBiasBench, BBQ, StereoSet, and CrowS-Pairs. Key results include:

• On the BBQ dataset:
  • Near 48% improvement in ambiguous QA accuracy.
  • 6% improvement in disambiguated QA accuracy, using adapters trained on just a small sample of data.
• Ablation studies demonstrate the critical role of the fusion layer and cross-category sharing in maximizing both debiasing and accuracy.
• Adapter modules trained on English data transfer zero-shot to other languages (e.g., Korean BBQ), achieving 84% accuracy and underscoring strong language-agnostic generalization.

Comprehensive tables and comparisons confirm that Open-DeBias not only achieves lower bias scores (closer to the neutral target) on StereoSet and CrowS-Pairs, but also preserves strong language modeling ability. Extensive evaluation across a diversity of bias domains in OpenBiasBench highlights its robustness for open-domain fairness.

5. Cross-Lingual and Zero-Shot Generalization

A distinctive outcome is Open-DeBias’s demonstrated cross-lingual transferability:

• Adapter modules fine-tuned solely on English QA datasets (BBQ) perform robustly when applied, in zero-shot fashion, to Korean BBQ (KoBBQ) context, with accuracy up to 84%.
• This suggests that the bias representations captured by adapters encode language-agnostic structural information, reducing reliance on token-level or category-specific annotations.

Such transfer is significant for practical deployment in multilingual scenarios and low-resource domains where direct bias annotation or retraining is infeasible.

6. Broader Implications and Future Work

The Open-DeBias framework enables scalable, efficient, and general-purpose debiasing for LLMs, offering several advantages:

• Does not require exhaustive or predefined bias annotation.
• Mitigates both known and novel forms of bias, supporting robust fairness under open-set or emergent conditions.
• Adapters can be modularly updated or extended, without retraining the core LLM.

Possible directions for future research identified in the work:

• Extending the framework to open-ended or generative QA, moving beyond multiple-choice formats.
• Adapting Open-DeBias to tasks beyond QA, such as natural language inference or paraphrase detection.
• Incorporating additional layers of human and cross-cultural evaluation to ensure equitable generalization across diverse sociolinguistic contexts.

7. Summary Table: Core Aspects of Open-DeBias

Component	Description	Significance
Adapter Modules	Lightweight, inserted pre/post-FFN per transformer layer	Parameter- and domain-efficiency, modularity
OpenBiasBench	473k+ QA items, 31+ categories, 10k subgroups	Evaluates open-set and emergent bias mitigation
Fusion Layer	Aggregates outputs from multiple adapters	Enables cross-bias and cross-lingual transfer
Ambiguity-Aware Loss	CE for disambiguated, KL to uniform for ambiguous contexts	Captures both overt and subtle bias scenarios
Cross-Lingual Transfer	Zero-shot performance (e.g., KoBBQ with English-trained adapters)	Demonstrates generalization and language-agnosticity

Open-DeBias represents a scalable architectural and methodological advance toward open-set bias mitigation in LLMs, validated across broad, multilingual settings and with rigorous evaluation protocols (Rani et al., 28 Sep 2025).