Why reviewing AI-generated code is devilishly hard (www.spinellis.gr)

In recent discussions on AI-generated code, experts highlight the challenges associated with reviewing such code due to cognitive biases and a lack of understanding. Unlike traditional programming, where a developer's comprehension acts as a control gate, AI-assisted coding can lead to false confidence in one's understanding of generated changes. The Dunning-Kruger effect and the illusion of explanatory depth indicate that junior programmers may overestimate their grasp of complex code, making them more susceptible to accepting flawed AI-generated modifications. Moreover, the inherent fluency of large language models (LLMs) can make erroneous code seem trustworthy, further complicating the review process. Human reviewers, especially those with more experience, often rely on intuition to identify errors, but the seeming plausibility of AI outputs can inhibit thorough verification. This phenomenon is compounded by automation bias, where users may unwittingly trust automated suggestions over their judgment. As a result, developers are encouraged to approach AI-generated code with heightened vigilance and a critical mindset to mitigate potential discrepancies and ensure code quality.