Inducing Group Fairness in LLM-Based Decisions
<|reference_start|>Inducing Group Fairness in LLM-Based Decisions: Prompting Large Language Models (LLMs) has created new and interesting means for classifying textual data. While evaluating and remediating group fairness is a well-studied problem in classifier fairness literature, some classical approaches (e.g., regularization) do not carry over, and some new opportunities arise (e.g., prompt-based remediation). We measure fairness of LLM-based classifiers on a toxicity classification task, and empirically show that prompt-based classifiers may lead to unfair decisions. We introduce several remediation techniques and benchmark their fairness and performance trade-offs. We hope our work encourages more research on group fairness in LLM-based classifiers.<|reference_end|>
@article{atwood2024inducing,
    title={Inducing Group Fairness in LLM-Based Decisions},
    author={James Atwood, Preethi Lahoti, Ananth Balashankar, Flavien Prost, Ahmad
  Beirami},
    journal={arXiv preprint arXiv:2406.16738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16738},
    primaryClass={cs.LG cs.AI cs.CY}
}