Debugging Overconfidence: Is AI Too Sure of Itself?

"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)."

"We don't usually think of machines in terms of metacognitive biases because these distortions entail self-reflection or self-judgment. But biases like overconfidence are highly relevant, because AI simulates human thought and is built by humans. Overconfidence can affect AI products like LLMs at both the development and user feedback stages."

"Take one of the most ubiquitous human biases: overconfidence, an excessive belief in one's own abilities, as evident in gaps between perceived and actual performance. Both LLMs themselves and users tend to overestimate the correctness of LLMs' answers."

AI systems inherit human cognitive biases through multiple pathways: biased training data from input selection, measurement, or labeling processes; developer assumptions embedded in model design; and user feedback loops that reinforce flawed outputs. Overconfidence—excessive belief in one's abilities despite gaps between perceived and actual performance—represents a particularly significant metacognitive bias affecting LLMs. Both the models themselves and their users tend to overestimate the correctness of LLM answers. This bias operates at development stages where developers may embed their own overconfidence into algorithms, and at user stages where anthropomorphic interactions lead people to trust AI outputs excessively. Addressing this requires coordinated strategies from both AI developers and vigilant users.

Read at Psychology Today