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Paper Number
1368
Paper Type
Complete
Abstract
State-of-the-art GPT models are being increasingly used in different domains. However, previous research has discovered stereotypical biases in their outputs. To address this, we proposes a two-phase approach. During Phase I, we use benchmark datasets to identify bias. Based on the literature, we define three metrics that consider both the preference toward stereotypes and the ability of models to refuse providing an answer when prompted with biased content. In Phase II, we examine the stability of the results from Phase I by an adversarial attack. We apply our approach to GPT-3.5 Turbo and GPT-4 using the BBQ and CrowS-Pairs benchmark datasets. The evaluation shows that both models are biased toward stereotypes with GPT-4 refusing to answer more often. However, if it does so, the results are more biased. Additionally, the adversarial attack could induce the models to provide an answer, but that did not impact the level of bias substantially.
Recommended Citation
Stephens Fracassi, Sofia Ines and Hristova, Diana, "Evaluation of Stereotypical Biases in Recent GPT Models" (2024). ICIS 2024 Proceedings. 6.
https://aisel.aisnet.org/icis2024/aiinbus/aiinbus/6
Evaluation of Stereotypical Biases in Recent GPT Models
State-of-the-art GPT models are being increasingly used in different domains. However, previous research has discovered stereotypical biases in their outputs. To address this, we proposes a two-phase approach. During Phase I, we use benchmark datasets to identify bias. Based on the literature, we define three metrics that consider both the preference toward stereotypes and the ability of models to refuse providing an answer when prompted with biased content. In Phase II, we examine the stability of the results from Phase I by an adversarial attack. We apply our approach to GPT-3.5 Turbo and GPT-4 using the BBQ and CrowS-Pairs benchmark datasets. The evaluation shows that both models are biased toward stereotypes with GPT-4 refusing to answer more often. However, if it does so, the results are more biased. Additionally, the adversarial attack could induce the models to provide an answer, but that did not impact the level of bias substantially.
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