Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendations
Large Language Models (LLMs) can infer sensitive attributes such as gender or age from indirect cues like names and pronouns, potentially biasing recommendations. While several debiasing methods exist, they require access to the LLMs' weights, are computationally costly, and cannot be used by lay users. To address this gap, we investigate implicit biases in LLM Recommenders (LLMRecs) and explore whether prompt-based strategies can serve as a lightweight and easy-to-use debiasing approach. We contribute three bias-aware prompting strategies for LLMRecs. To our knowledge, this is the first study on prompt-based debiasing approaches in LLMRecs that focuses on group fairness for users. Our experiments with 3 LLMs, 4 prompt templates, 9 sensitive attribute values, and 2 datasets show that our proposed debiasing approach, which instructs an LLM to be fair, can improve fairness by up to 74% while retaining comparable effectiveness, but might overpromote specific demographic groups in some cases.
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Mihaela Rotar
69b7bff891a28162b233fd21
Theresia Veronika Rampisela
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2026-03-16T10:04:36.792Z
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/avatars/dbbf67f27593a510177dff06b00bf098.svg
TVR
theresiavr
user
theresiavr
69b7bff891a28162b233fd22
Maria Maistro
Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendations
Large Language Models (LLMs) can infer sensitive attributes such as gender or age from indirect cues like names and pronouns, potentially biasing recommendations. While several debiasing methods exist, they require access to the LLMs' weights, are computationally costly, and cannot be used by lay users. To address this gap, we investigate implicit biases in LLM Recommenders (LLMRecs) and explore whether prompt-based strategies can serve as a lightweight and easy-to-use debiasing approach. We contribute three bias-aware prompting strategies for LLMRecs. To our knowledge, this is the first study on prompt-based debiasing approaches in LLMRecs that focuses on group fairness for users. Our experiments with 3 LLMs, 4 prompt templates, 9 sensitive attribute values, and 2 datasets show that our proposed debiasing approach, which instructs an LLM to be fair, can improve fairness by up to 74% while retaining comparable effectiveness, but might overpromote specific demographic groups in some cases.
Three bias-aware prompting strategies are investigated for large language model recommenders to improve group fairness while maintaining effectiveness.
large language models LLM recommenders implicit biases prompt-based strategies group fairness debiasing methods sensitive attributes demographic groups