The notion of moral expertise poses a variety of challenges concerning both the question of existence of such experts and their identification by laypeople. I argue for a view of ethics expertise, based on moral understanding instead of on moral knowledge, that is less robust than genuine moral expertise and that does not rely on deference to testimony. I propose identification criteria that focus mainly on the awareness and communication of implicit biases and situated ignorance. According to the account of (...) ethics expertise presented in this paper, the expert's testimony is not an epistemic reason for the layperson's belief, but merely an epistemic influence. The epistemic reasons for the layperson's belief are largely independent from the expert. But there is still some epistemic risk involved in the proposed method of knowledge transfer, and therefore criteria for the identification of a trustworthy expert are necessary. The risk involved in knowledge transfer can be both due to willful manipulation and due to the expert's implicit biases and situated ignorance. While willful manipulation cannot really be avoided, the influence from biases and ignorance can be minimized. I argue that the best way to do this is if the expert is aware of their own biases and ignorance and communicates them. Combined with evidence of the expert's education in moral philosophy and experience with the topic in question, this gives the layperson the best chance to identify someone who can really help them consider all relevant aspects of a situation and come to a better justified decision. (shrink)

Presents a theory of bullshit, how it differs from lying, how those who engage in it change the rules of conversation, and how indulgence in bullshit can alter a person's ability to tell the truth.

Large language models (LLMs) such as ChatGPT or Google’s Bard have shown significant performance on a variety of text-based tasks, such as summarization, translation, and even the generation of new...

ChatGPT has taken the academic community by storm (Cotton, Cotton, and Shipway 2023; Cox and Tzoc 2023; Sullivan, Kelly, and McLaughlan 2023). Since its release in November 2022, chatGPT has predic...

In the last several months, several major disciplines have started their initial reckoning with what ChatGPT and other Large Language Models (LLMs) mean for them – law, medicine, business among other professions. With a heavy dose of humility, given how fast the technology is moving and how uncertain its social implications are, this article attempts to give some early tentative thoughts on what ChatGPT might mean for bioethics. I will first argue that many bioethics issues raised by ChatGPT are similar (...) to those raised by current medical AI – built into devices, decision support tools, data analytics, etc. These include issues of data ownership, consent for data use, data representativeness and bias, and privacy. I describe how these familiar issues appear somewhat differently in the ChatGPT context, but much of the existing bioethical thinking on these issues provides a strong starting point. There are, however, a few “new-ish” issues I highlight – by new-ish I mean issues that while perhaps not truly new seem much more important for it than other forms of medical AI. These include issues about informed consent and the right to know we are dealing with an AI, the problem of medical deepfakes, the risk of oligopoly and inequitable access related to foundational models, environmental effects, and on the positive side opportunities for the democratization of knowledge and empowering patients. I also discuss how races towards dominance (between large companies and between the U.S. and geopolitical rivals like China) risk sidelining ethics. (shrink)

According to Russell and Norvig’s (2009) classification, Artificial Intelligence (AI) is the field that aims at building systems which either think rationally, act rationally, think like humans, or...