Inducing Group Fairness in Prompt-Based Language Model Decisions (2406.16738v2)

Abstract: Classifiers are used throughout industry to enforce policies, ranging from the detection of toxic content to age-appropriate content filtering. While these classifiers serve important functions, it is also essential that they are built in ways that minimize unfair biases for users. One such fairness consideration is called group fairness, which desires that different sub-population of users receive equal treatment. This is a well-studied problem in the context of 'classical' classifiers. However, the emergence of prompt-based LLM (LM) decision making has created new opportunities to solve text-based classification tasks, and the fairness properties of these new classifiers are not yet well understood. Further, the `remediation toolkit' is incomplete for LM-based decision makers and little is understood about how to improve decision maker group fairness while maintaining classifier performance. This work sets out to add more tools to that toolbox. We introduce adaptations of existing effective approaches from the classical classifier fairness to the prompt-based classifier space. We also devise simple methods that take advantage of the new structure of prompt-based decision makers and operate at the prompt level. We compare these approaches empirically on real data. Our results suggest that adaptations of approaches that are effective for classical classifiers remain effective in the LM-based classifier environment. However, there is room for further exploration of prompt-based remediation methods (and other remediation methods that take advantage of LM structure).

• The paper demonstrates that LLM toxicity classifiers exhibit significant fairness gaps, with FPR disparities up to 124% in zero-shot and few-shot settings.
• The paper introduces and evaluates prompt-based, in-processing, and post-processing methods to mitigate group bias in LLM outputs.
• The paper’s results emphasize the urgent need for advanced fairness strategies in high-stakes applications, inspiring further research on parameter-efficient tuning.

Inducing Group Fairness in LLM-Based Decisions: An Overview

The paper "Inducing Group Fairness in LLM-Based Decisions" by Atwood et al., presents an insightful examination of fairness in LLM-based classifiers, specifically focusing on the toxicity classification task. Despite the extensive paper on group fairness in classical classifiers, the authors identify a significant gap in the extant literature concerning LLM-based classifiers. This work seeks to fill that gap by providing empirical evidence and proposing remediation techniques to achieve group fairness in zero-shot and few-shot classification settings.

Key Findings

The paper measures fairness in terms of Equality of Opportunity (EO), focusing on the False Positive Rate (FPR) across different demographic groups. The findings reveal substantial disparities in FPR, particularly for Muslim and Jewish groups compared to the Christian group, when using both zero-shot and few-shot LLM-based classifiers.

• Zero-shot classification yields an 89% higher FPR for the Muslim group and a 48% higher FPR for the Jewish group.
• Few-shot classification exacerbates this disparity, with a 124% higher FPR for the Muslim group and a 71% higher FPR for the Jewish group.

These results underline the pressing need for effective fairness remediation techniques for LLM-based classifiers.

Remediation Techniques

The paper introduces three primary remediation techniques:

1. Prompt-based Methods:
   • PBF: "Please be as fair as possible when making a decision."
   • PBF2SG: "Please be as fair as possible when making a decision about comments about religious groups or that mention religion."
   • PBF2TG: "Please be as fair as possible when making a decision about comments that mention Judaism or Jewish people."
2. In-Processing Methods:
   • Implemented with a Maximum Mean Discrepancy (MMD) regularizer during the fine-tuning phase of the model to align the FPR distributions of different demographic groups.
3. Post-Processing Methods:
   • Applied after model predictions to adjust the probability estimates and achieve a better fairness-performance trade-off.

Experimental Setup and Results

The experiments are conducted using the Civil Comments Identity dataset. The results indicate that:

• Prompt-based methods were ineffective in significantly reducing FPR disparities. Even the most focused prompt led to only a 40% higher FPR for the Muslim and Jewish groups compared to the Christian group.
• In-processing methods outperformed post-processing methods in achieving a favorable fairness-performance trade-off in fine-tuned settings.
• Fine-tuning the classifier with in-processing methods consistently closed the fairness gap more effectively than post-processing methods.
• For transfer tasks, where the in-processing method is not applicable, the post-processing method still offered measurable fairness improvements, though with some performance degradation.

Implications and Future Directions

The implications of this research are both practical and theoretical. On a practical level, the paper highlights the challenges and potential strategies for achieving group fairness in LLM-based classifiers, which are increasingly being deployed in high-stakes domains such as finance and healthcare. Theoretically, the work expands the domain of fairness in AI by considering the unique challenges posed by LLMs, especially in zero-shot and few-shot settings.

Future research should explore more sophisticated prompting strategies, potentially leveraging advances in LLM capabilities and emergent behaviors. Additionally, evaluating these methods across a broader range of datasets and languages will be crucial for generalizing the findings. The authors also hint at the importance of developing more parameter-efficient fine-tuning techniques, such as low-rank adaptations and prompt-tuning, which could offer new avenues for achieving fairness without extensive computational overhead.

Conclusion

The paper provides a critical assessment of the fairness of LLM-based classifiers and introduces methods for mitigating observed disparities. While prompt-based methods offer limited remediation, in-processing and post-processing methods, particularly when fine-tuning is allowable, demonstrate significant potential for improving group fairness. The paper serves as a clarion call for further research and development in this essential aspect of AI fairness, laying a groundwork that future studies can build upon. The advancements in LLM capabilities will likely play a crucial role in enhancing these fairness remediation techniques in the coming years.