Strategies developed by the artificial intelligence (AI) community for dealing with ethical issues should be useful in dealing with neurotechnology. Some ethical issues associated with both AI and neurotech are illustrated by an application:

• “Ensuring a representative group of patients [when testing]”;
• “Including a diverse set of engineers [when designing]”;
• “Mitigating potential bias in the AI algorithms”;
• “Verifying ... that certain populations are not disproportionately impacted by its side effects”; and
• “Obtaining feedback and input from a diverse set of [participants].”

The AI community has wrestled with these issues for some time and has developed insights and strategies. (The application described aims to mitigate epilepsy by both applying signals to the brain and measuring brain voltages. AI is used to analyze the voltages and to design the signals applied.)

Of course, some ethical issues inherent to neurotech do not completely correspond to AI issues. One example cited is long-term influence on a patient’s sense of identity after prolonged neuromodulation. Another example concerns “fairness,” which can be much more significant in neurotechnology situations than in AI because of increased intimacy. Fairness also includes issues of expense and access.

The article lists 14 core neuroethics issues, and suggests a multi-stakeholder effort to identify corresponding AI issues and to transfer AI knowledge and capabilities to neurotechnology. It includes a review of core AI ethical issues, of neurotechnology, and of how AI is being used in neurosciences. While its basic premise appears promising, the article is somewhat hard to read, perhaps because it describes so much.