Social Bias in LLM-Generated Code: Benchmark and Mitigation
📰 ArXiv cs.AI
Learn to identify and mitigate social bias in LLM-generated code with a new benchmark and mitigation strategies, crucial for fair human-centered applications
Action Steps
- Build a dataset of coding tasks using SocialBias-Bench to evaluate LLMs for social bias
- Run experiments to assess the prevalence of social bias in LLM-generated code
- Configure mitigation strategies such as data preprocessing and regularization techniques to reduce social bias
- Test the effectiveness of mitigation strategies on a held-out dataset
- Apply fairness metrics to evaluate the performance of LLMs on socially sensitive tasks
Who Needs to Know This
AI engineers, data scientists, and software developers working on human-centered applications can benefit from understanding social bias in LLM-generated code to ensure fairness and equity in their products
Key Insight
💡 Social bias in LLM-generated code can perpetuate existing social inequalities, and mitigation strategies are necessary to ensure fairness and equity
Share This
🚨 New benchmark & mitigation strategies for social bias in LLM-generated code! 🚨 Ensure fairness in human-centered apps #LLMs #SocialBias #Fairness
Key Takeaways
Learn to identify and mitigate social bias in LLM-generated code with a new benchmark and mitigation strategies, crucial for fair human-centered applications
Full Article
Title: Social Bias in LLM-Generated Code: Benchmark and Mitigation
Abstract:
arXiv:2605.00382v2 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed to generate code for human-centered applications where demographic fairness is critical. However, existing evaluations focus almost exclusively on functional correctness, leaving social bias in LLM-generated code largely unexamined. Extending our prior work on Solar, we conduct a comprehensive empirical study using SocialBias-Bench, a benchmark of 343 real-world coding tasks spanning seven de
Abstract:
arXiv:2605.00382v2 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed to generate code for human-centered applications where demographic fairness is critical. However, existing evaluations focus almost exclusively on functional correctness, leaving social bias in LLM-generated code largely unexamined. Extending our prior work on Solar, we conduct a comprehensive empirical study using SocialBias-Bench, a benchmark of 343 real-world coding tasks spanning seven de
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