Thread by @mer__edith

[Modest Thread Alert] Late, but thrilled to be a coauthored on this short piece outlining ethical priorities for neurotech and AI. An important step: AI models provide the foundation on which technologies like brain-computer interfaces increasingly rely. nature.com/news/four-ethi…

With that, some additional comments, clarifying my personal position since some of my more nuanced edits aren't reflected in the published version...

First, I think it's important that the call for regulation extend beyond the transfer and reuse of neural data. To be effective, it must also apply to models trained with this data.

Also essential that we pay close to how the "normal human brain" is modeled and standardized. Which assumptions, cultures, and individuals define normal, and what are the implications of universalizing such definitions? This is at the heart of questions of bias.

It is also imperative that we don't define the rights of patients simply as rights to be educated.

We must incorporate clear acknowledgements of power imbalances between those producing and profiting from such technologies, and those on whom they will act, and draft processes of consent accordingly.

I'd also strongly assert that education and exposure to ethical concepts are not sufficient to ensure that designers and developers behave ethically. Engineers reading Habermas in grad school is not enough!

We need clear and specific incentive structures that require ethical behavior, and which acknowledge power relationships. These must be backed by sound regulation and explicitly linked to accepted methods for measuring and assessing ethical outcomes.

Those who design and profit from these systems must ultimately bear responsible. Just as malpractice can lead to doctors losing their license, ethical codes must be more than "information" -- they must come with consequences.

And these codes and consequences must be validated and put in place BEFORE this technology is used on experimental subjects (i.e. anyone). FIN. TY.