Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Uncovering and Quantifying Social Biases in Code Generation
Authors: Yan Liu, Xiaokang Chen, Yan Gao, Zhe Su, Fengji Zhang, Daoguang Zan, Jian-Guang Lou, Pin-Yu Chen, Tsung-Yi Ho
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose a new paradigm to construct code prompts and successfully uncover social biases in code generation models. To quantify the severity of social biases in generated code, we develop a dataset along with three metrics to evaluate the overall social bias and fine-grained unfairness across different demographics. Experimental results on three pre-trained code generation models (Codex, In Coder, and Code Gen) with varying sizes, reveal severe social biases. |
| Researcher Affiliation | Collaboration | Microsoft Research Peking University The Chinese University of Hong Kong IBM Research EMAIL, EMAIL, zhesu@EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and processes but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code, trained classifier, and data are available at https://github.com/the Namek/Code-Bias.git. |
| Open Datasets | Yes | Our code, trained classifier, and data are available at https://github.com/the Namek/Code-Bias.git. |
| Dataset Splits | Yes | Annotated data is randomly partitioned into train, development, and test sets with a ratio of 7 : 2 : 1. |
| Hardware Specification | No | The paper mentions running experiments and evaluating models, but it does not specify any particular hardware details such as GPU models, CPU types, or memory configurations used for the experiments. |
| Software Dependencies | No | The paper mentions using specific models like 'BERT-Base [7]' and 'word2vec' for classifiers, but it does not provide specific version numbers for these or other software dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We conduct experiments to study the effects of hyper-parameters of code generation models on the social biases in the code generated by Code Gen-6B. We mainly analyze two hyper-parameters: temperature t [1] and top-p [14]... We set the values of temperature t from {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}... We set the values of top-p from {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}. |