When actions are mediated by means of a brain-computer interface, it seems that we cannot assess whether the user is culpable for the ac- tion without determining whether the brain-computer interface correctly decoded the intentions of the user. Here I argue that this requirement is confused. I also argue that, at least for the purposes of assessing moral culpability, BCI-mediated action should be viewed on the model of action mediated by ordinary (albeit complex) tools.

A pressing worry in the ongoing neurorights debate is the language used to advocate for newly proposed rights. This paper addresses this concern by first examining the partial and ambiguous associations between mind reading and neurotechnology, often cited by advocates in support of the right to mental privacy. Secondly, it addresses the conceptual foundations of mind reading, distinguishing between natural, digital, and neurotechnological forms. These distinctions serve to highlight the normative parallels in privacy vulnerabilities between neurotechnology and other mind-reading methods, (...) with an emphasis on multimodal digital systems. I suggest that authentic safeguards for the mental realm demand an expansion of the protective ambit beyond brain-targeted devices to recognize the spectrum of mind-reading applications. Ultimately, this urges re-evaluation of the scope and justification of a right to mental privacy owing to the need for coherent frameworks in an increasingly interconnected digital landscape. (shrink)

This article examines the idea of mind-reading technology by focusing on an interesting case of applying a large language model (LLM) to brain data. On the face of it, experimental results appear to show that it is possible to reconstruct mental contents directly from brain data by processing via a chatGPT-like LLM. However, the author argues that this apparent conclusion is not warranted. Through examining how LLMs work, it is shown that they are importantly different from natural language. The former (...) operates on the basis of nonrational data transformations based on a large textual corpus. The latter has a rational dimension, being based on reasons. Using this as a basis, it is argued that brain data does not directly reveal mental content, but can be processed to ground predictions indirectly about mental content. The author concludes that this is impressive but different in principle from technology-mediated mind reading. The applications of LLM-based brain data processing are nevertheless promising for speech rehabilitation or novel communication methods. (shrink)