FairCoder: Evaluating Social Bias of LLMs in Code Generation (2501.05396v2)

Abstract: LLMs have been widely deployed in coding tasks, drawing increasing attention to the evaluation of the quality and safety of LLMs' outputs. However, research on bias in code generation remains limited. Existing studies typically identify bias by applying malicious prompts or reusing tasks and dataset originally designed for discriminative models. Given that prior datasets are not fully optimized for code-related tasks, there is a pressing need for benchmarks specifically designed for evaluating code models. In this study, we introduce FairCoder, a novel benchmark for evaluating social bias in code generation. FairCoder explores the bias issue following the pipeline in software development, from function implementation to unit test, with diverse real-world scenarios. Additionally, three metrics are designed to assess fairness performance on this benchmark. We conduct experiments on widely used LLMs and provide a comprehensive analysis of the results. The findings reveal that all tested LLMs exhibit social bias.

• The paper introduces FairCode, a new benchmark and FairScore metric to specifically evaluate and quantify social biases in Large Language Models used for code generation.
• FairCode includes two evaluation tasks, Function Implementation and Test Case Generation, designed to identify subtle biases related to sensitive attributes in model outputs.
• Findings reveal biases across popular LLMs, showing better handling of traditional biases (gender, race) but heightened bias in emergent attributes (age, income) and test case generation tasks.

An Analysis of FairCode: Social Bias in LLMs for Code Generation

The paper "FairCode: Evaluating Social Bias of LLMs in Code Generation" by Du et al. introduces FairCode, a benchmark designed to evaluate the social biases present within LLMs used for code generation. The research acknowledges the existing capabilities of LLMs in generating code and underscores the necessity for benchmarks that specifically assess their performance from a fairness perspective. FairCode emerges as a significant advancement in this space, bringing into focus the limitations in code generation that could perpetuate societal biases and stereotypes.

Methodology

The authors propose the inclusion of two distinct tasks in the FairCode benchmark:

1. Function Implementation: This involves using few-shot prompting to encourage LLMs to generate bias-free code. The task evaluates how LLMs score candidates across various social contexts such as job hiring, college admissions, and medical treatment based on non-sensitive attributes. The critical insight here is to observe the model's implementation to ensure it does not inadvertently favor certain demographic groups over others.
2. Test Case Generation: This task prompts LLMs to create test cases for predefined functions, assessing individuals’ attributes like health or social status without imparting biased correlations with sensitive attributes. This aspect sheds light on the subtle biases LLMs manifest when associating specific demographic attributes with the scenarios presented.

Core Metrics

The paper introduces a novel metric, FairScore, which aims to quantify bias by combining the model's refusal rate (the frequency with which the model avoids using sensitive attributes) with its preference entropy (the distribution of model decisions across subgroups). This metric offers a robust, multifaceted approach to evaluate the fairness of LLM outputs, indicating a model's tendency to prefer certain demographic groups in response generation.

Findings

The experimental results presented in the paper highlight notable biases across various LLMs, including popular models such as Llama, Mistral, and GPT. The models display disparate performance levels, with some exhibiting higher refusal rates but lower entropy, indicating ongoing biases. Prominent findings include:

• Better Handling of Traditional Biases: LLMs generally perform better in avoiding gender and race biases in familiar contexts, such as job hiring and college admissions.
• Emergent Biases: There is heightened bias in less scrutinized attributes, such as age, income level, or parental degree in education settings—areas that require more rigorous alignment and fine-tuning processes.
• Test Case Generation Challenges: The research demonstrates increased biases in test case generation, underscoring the need for LLM advancements and better alignment strategies in complex tasks.

Implications and Future Directions

The research paper has several implications for both academia and industry. Practically, the insights from FairCode point towards the necessity for improved model training that incorporates ethical considerations and fairness across demographic attributes. The concept of FairScore identifies new dimensions for evaluating the fairness of AI systems, pushing the boundaries on how biases are understood and mitigated.

Theoretically, the paper opens pathways for further research on how LLMs can be trained to reduce discriminatory tendencies, especially in evolving domains like code generation. Additionally, the analysis calls for a broader scope in testing datasets, ensuring they encompass a wide range of attributes and societal biases.

Future work could build on FairCode by introducing more fine-grained assessments, incorporating diverse real-world datasets, and establishing comprehensive evaluation frameworks that address both explicit and implicit biases. Ultimately, FairCode establishes a foundational step towards creating equitable AI systems that responsibly produce code without perpetuating harmful stereotypes.

This work serves as a reminder of the continuous need for refinement in AI models to align them more closely with human-centered values in code generation contexts.