The epistemic debt of generative AI

Problem — The paper addresses the unexamined reliance on generative AI in cognitive tasks, leading to a gap between presented outputs and defensible knowledge. This phenomenon, termed ‘epistemic debt,’ emerges when subsequent work builds on these unverified outputs, potentially compounding inaccuracies. The work is presented as a preprint, indicating it has not yet undergone peer review.

Method — The authors conceptualize epistemic debt as a framework for understanding the implications of generative AI outputs in cognitive tasks. They analyze the cognitive processes involved in using AI-generated content and the responsibilities of authors in critically evaluating these outputs. The paper does not disclose specific architectures, loss functions, or training compute, focusing instead on theoretical implications rather than empirical methods.

Results — The available text does not report quantitative results.

Limitations — The authors acknowledge that the concept of epistemic debt is still in its formative stages and may require further empirical validation. Additionally, the lack of quantitative analysis limits the ability to measure the extent of epistemic debt in practical scenarios.

Why it matters — Understanding epistemic debt is crucial for researchers and practitioners who utilize generative AI, as it emphasizes the need for critical engagement with AI outputs to avoid propagating inaccuracies. This work lays the groundwork for future studies on the cognitive implications of AI in research practices, as published in Nature Machine Intelligence.