Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5 Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students

• Published in: Big data and cognitive computing Vol. 7; no. 3; p. 124
• Main Authors: Abramski, Katherine, Citraro, Salvatore, Lombardi, Luigi, Rossetti, Giulio, Stella, Massimo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2023
• Subjects: Academic disciplines; Anxiety; Artificial intelligence; Bias; Cognition & reasoning; cognitive networks; Cognitive psychology; Computational linguistics; Criminal investigations; Gender; High school students; Language; Language processing; Large language models; math anxiety; Mathematics anxiety; Natural language interfaces; Perceptions; Science; Semantics; Society; STEM education; Stereotypes; Students; United Kingdom
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc7030124

Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI’s models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.