Brain-to-AI adaptive feedback systems refer to a class of technologies in which neurophysiological signals from human brains are used in real time to adapt the behavior of artificial intelligence systems, creating closed‐loop feedback that can adjust according to the mental, emotional, or cognitive state of the user. These systems sit at the intersection of brain-computer interfaces (BCIs), neurofeedback, affective computing, adaptive learning, and AI, and promise to transform domains ranging from education and rehabilitation to human–machine collaboration and mental health. But with great promise come profound technical, ethical, and societal challenges: issues of signal fidelity and latency; interpretability and trust; individual variability; data privacy and autonomy; potential for bias and misuse. In this opinion piece I explore the potential benefits of brain-to-AI adaptive feedback systems, the key obstacles they face, and the governance, design, and value judgments that must guide their development if they are to enhance human well-being rather than undermine it.
Rachel Eglinton (Thu,) studied this question.