2026/06/23 by Will Fleisher · 1 voice
Social Sciences · Computer Science · #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #Adversarial Robustness in Machine Learning
paper · doi:10.1145/3805689.3812360
There is enormous excitement surrounding AI technology. This excitement is driven, at least in part, by hype: exaggerated claims about the capabilities of AI. Since AI is an inherently interesting and sensational topic, and since there has been a lot of hype claims made about it, discussions of AI in public discourse are suffused with hype. These hype conditions have significant potential consequences concerning distribution of resources and environmental damage. This paper argues that these hype conditions also pose a distinct risk for researchers who work on AI. In short, the risk is that, in hype conditions, ordinary research activities and communication may unintentionally contribute to hype. Research is often exploratory or involves arguing for hypotheses that are not accepted as scientific consensus. Furthermore, there is always a chance that even excellent science will result in error; this is known as inductive risk. However, not all errors have the same consequences. Sometimes, accepting a false hypothesis can have worse consequences than rejecting a true one, or vice versa. A researcher might, quite reasonably, argue in a published article for a hypothesis about AI that turns out to be false. At the same time, because of the hype conditions surrounding AI, the article may be read by a much wider audience than they intend. Thus, when arguing for hypotheses, researchers run an inductive risk of contributing to AI hype. This risk needs to be considered by researchers when deciding how to conduct inquiry and communicate their results.