AI inherits human cognitive biases through training data, model assumptions, and user feedback loops.

Overconfidence, a characteristically human metacognitive bias, increasingly appears in LLM outputs.

Both LLMs themselves and users tend to overestimate the correctness of LLMs' answers.

Mitigation requires both developer strategies and user vigilance.

With the rising popularity of AI, particularly Large Language Models (LLMs), there has been a lot of talk about bias. Since AI is made by humans, our cognitive shortcomings can easily creep into AI technology. When training data become biased in input selection, measurement, or labeling processes, algorithms and outputs will be biased, too. Frequently mentioned areas in which biases occur include demographics (e.g., gender stereotypes or underrepresentation of minorities) and culture (e.g., Western-centric norms). When it comes to users' perceptions of AI, the concern of anthropomorphic bias has also become an important area of discussion. This bias arises because humans interact with AI (e.g., Chat GPT) as if it were a human being. AI, in turn, learns to play to those expectations.

There are other biases in human thinking that may not be highlighted sufficiently with respect to AI algorithms. Take one of the most ubiquitous human biases: overconfidence, an excessive belief in one’s own abilities, as evident in gaps........

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