On bullshit, large language models, and the need to curb your enthusiasm

Tigard, D. W. (2025).

AI And Ethics.

https://doi.org/10.1007/s43681-025-00743-3

Abstract

Amidst all the hype around artificial intelligence (AI), particularly regarding large language models (LLMs), generative AI and chatbots like ChatGPT, a surge of headlines is instilling caution and even explicitly calling “bullshit” on such technologies. Should we follow suit? What exactly does it mean to call bullshit on an AI program? When is doing so a good idea, and when might it not be? With this paper, I aim to provide a brief guide on how to call bullshit on ChatGPT and related systems. In short, one must understand the basic nature of LLMs, how they function and what they produce, and one must recognize bullshit. I appeal to the prominent work of the late Harry Frankfurt and suggest that recent accounts jump too quickly to the conclusion that LLMs are bullshitting. In doing so, I offer a more level-headed approach to calling bullshit, and accordingly, a way of navigating some of the recent critiques of generative AI systems.

Here are some thoughts:

This paper examines the application of Harry Frankfurt's theory of "bullshit" to large language models (LLMs) like ChatGPT. It discusses the controversy around labeling AI-generated content as "bullshit," arguing for a more nuanced approach. The author suggests that while LLM outputs might resemble bullshit due to their lack of concern for truth, LLMs themselves don't fit the definition of a "bullshitter" because they lack the intentions and aims that Frankfurt attributes to human bullshitters.

For psychologists, this distinction is important because it asks for a reconsideration of how we interpret and evaluate AI-generated content and its impact on human users. The paper further argues that if AI interactions provide tangible benefits to users without causing harm, then thwarting these interactions may not be necessary. This perspective encourages psychologists to weigh the ethical considerations of AI's influence on individuals, balancing concerns about authenticity and integrity with the potential for AI to enhance human experiences and productivity.

'You can't bullshit a bullshitter' (or can you?): Bullshitting frequency predicts receptivity to various types of misleading information

Littrell, S., Risko, E. F., & Fugelsang, J. A. (2021).

The British journal of social psychology, 60(4), 

1484–1505.

https://doi.org/10.1111/bjso.12447

Abstract

Research into both receptivity to falling for bullshit and the propensity to produce it have recently emerged as active, independent areas of inquiry into the spread of misleading information. However, it remains unclear whether those who frequently produce bullshit are inoculated from its influence. For example, both bullshit receptivity and bullshitting frequency are negatively related to cognitive ability and aspects of analytic thinking style, suggesting that those who frequently engage in bullshitting may be more likely to fall for bullshit. However, separate research suggests that individuals who frequently engage in deception are better at detecting it, thus leading to the possibility that frequent bullshitters may be less likely to fall for bullshit. Here, we present three studies (N = 826) attempting to distinguish between these competing hypotheses, finding that frequency of persuasive bullshitting (i.e., bullshitting intended to impress or persuade others) positively predicts susceptibility to various types of misleading information and that this association is robust to individual differences in cognitive ability and analytic cognitive style.

Conclusion

Gaining a better understanding of the differing ways in which various types of misleading information are transmitted and received is becoming increasingly important in the information age (Kristansen & Kaussler, 2018). Indeed, an oft-repeated maxim in popular culture is, “you can’t bullshit a bullshitter.” While folk wisdom may assert that this is true, the present investigation suggests that the reality is a bit more complicated. Our primary aim was to examine the extent to which bullshitting frequency is associated with susceptibility to falling for bullshit. Overall, we found that persuasive bullshitters (but not evasive bullshitters) were more receptive to various types of bullshit and, in the case of pseudo-profound statements, even when controlling for factors related to intelligence and analytic thinking. These results enrich our understanding of the transmission and detection of certain types of misleading information, specifically the associations between the propensity to produce and the tendency to fall for bullshit and will help to inform future research in this growing area of scholarship.

A pdf is here.