# How Reasoning Shapes Social Bias in LLM-Generated Code?

arXiv: https://arxiv.org/abs/2608.06829 · HTML: https://mlsanity.com/paper/2608.06829/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 20 of 20)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models](https://mlsanity.com/paper/2503.16419/) (2025)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Phi-4-reasoning Technical Report](https://mlsanity.com/paper/2504.21318/) (2025)
- [Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs](https://mlsanity.com/paper/2503.15341/) (2025)
- [FairCoder: Probing LLM Bias in High-Stakes Decision Making via Coding Tasks](https://mlsanity.com/paper/2501.05396/) (2025)
- [Mitigating Gender Bias in Machine Translation through Adversarial Learning](https://mlsanity.com/paper/2203.10675/) (2022)
- [A Simple, Yet Effective Approach to Finding Biases in Code Generation](https://mlsanity.com/paper/2211.00609/) (2022)
- [Reasoning with LLMs for Zero-Shot Vulnerability Detection](https://mlsanity.com/paper/2503.17885/) (2025)
- [Vul-R2: A Reasoning LLM for Automated Vulnerability Repair](https://mlsanity.com/paper/2510.05480/) (2025)
- [Mitigating Gender Bias in Code Large Language Models via Model Editing](https://mlsanity.com/paper/2410.07820/) (2024)
- [NEAT: Neuron-Based Early Exit for Large Reasoning Models](https://mlsanity.com/paper/2602.02010/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly used for code generation, yet generated programs may exhibit social bias through unfair or differential treatment of sensitive demographic attributes. While prior work mainly studies direct code generation, bias in reasoning-based generation remains underexplored. We conduct the first systematic study of social bias in reasoning-based code generation, evaluating 9 standard LLMs and large reasoning models (LRMs) on realistic bias-sensitive tasks across three human-centered decision scenarios. We find that reasoning generally reduces bias, lowering the average bias rate from 0.64 to 0.40, but the effect varies substantially across models. Meanwhile, code quality is not consistently preserved, with the average quality dropping from 0.72 to 0.59. Biased reasoning strongly predicts biased code, and adjusting generation configurations alone is insufficient for robust mitigation. Based on these findings, we propose ProbeDebias, a reasoning-aware framework that detects and rewrites biased reasoning traces before code generation. ProbeDebias achieves 87.76% F1 for reasoning-bias detection and reduces code bias by 83.73% on average while largely preserving quality. Compared with SOTA baselines, it further reduces average bias by 52.70%-54.42% and improves quality by 9.79%-36.79%. These results highlight the value of reasoning-stage analysis for trustworthy code generation.