Tigard, D. W. (2025).
AI And Ethics.
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.