The rapid advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) has led to their increasing integration into various domains, from text generation and translation to question-answering. However, a critical question remains: do these sophisticated models, much like humans, exhibit susceptibility to cognitive biases? Understanding the presence and nature of such biases in AI is paramount for assessing their reliability, enhancing their performance, and predicting their societal impact. This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek, two prominent LLMs, to framing effects and confirmation bias. The study meticulously designed a series of experimental trials, systematically manipulating information proportions and presentation orders to evaluate these biases.
In the framing effect experiment, a genetic testing decision-making scenario was constructed. The proportion of positive and negative information (e.g., 20%, 50%, or 80% positive) and their presentation order were varied. The models’ inclination towards undergoing genetic testing was recorded. For the confirmation bias experiment, two reports—one positive and one negative—about “RoboTaxi” autonomous vehicles were provided. The proportion of erroneous information within these reports (10%, 30%, and 50%) and their presentation order were systematically altered, and the models’ support for each report was assessed.
The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects. In the genetic testing scenario, their decision-making was primarily influenced by the proportion of positive and negative information presented. When the proportion of positive information was higher, both models showed a greater inclination to recommend or proceed with genetic testing. Conversely, a higher proportion of negative information led to greater caution or a tendency not to recommend the testing. Importantly, the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.
Regarding confirmation bias, the two models exhibited distinct behaviors. Gemini 1.5 Pro did not show an overall preference for either positive or negative reports. However, its judgments were significantly influenced by the order of information presentation, demonstrating a “recency effect,” meaning it tended to support the report presented later. The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions. In contrast, DeepSeek exhibited an overall confirmation bias, showing a clear preference for positive reports. Similar to Gemini 1.5 Pro, DeepSeek’s decisions were also significantly affected by the order of information presentation, while the proportion of misinformation had no significant effect.
These results reveal human-like cognitive vulnerabilities in advanced LLMs, highlighting critical challenges to their reliability and objectivity in decision-making processes. Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information, coupled with its sensitivity to order, underscore the need for careful evaluation of potential cognitive biases during the development and application of AI. The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts. Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena. The findings contribute significantly to understanding the limitations and capabilities of current AI systems, guiding their responsible development, and anticipating their potential societal implications.
Key words
artificial intelligence /
large language models /
cognitive bias /
confirmation bias /
framing effect
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Funding
* This work was supported by the Guangdong Provincial Undergraduate Teaching Quality and Teaching Reform Project (Yue Jiao Gao Han [2024] No. 30) and the Ministry of Education of China, Humanities and Social Sciences Research Project (22YJCZHI82).
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