Creating Ethical AI with a neuroscientific understanding of mindfulness combined with a critical theory-informed view of causality and neurosymbolic AI.

The discovery of DNA was a scientific accomplishment that undoubtedly changed the world. Disappointingly, one of the three men who received the Nobel prize for the discovery, James Watson, has a long history of racist, sexist, homophobic and anti-Semitic remarks (source). Despite the passage of time, protected by his privilege, his ignorant views have remained consistent. In a PBS documentary released in 2019, Watson says, “[t]here’s a difference on the average between blacks and whites in IQ tests. I would say the difference is genetic.” (source). Why does such an important scientific contribution have to be aligned with a white supremacist? What does it say about our society that every biology textbook myopically credits Watson as a progenitor of genetics, when his bias reveals how poorly he understands the subject?

When I first became fascinated with genetics as a teenager, I recall bristling at this unfairness and struggling to situate societal injustice within the purported objectiveness of science. In the years since, I have gained a better understanding of prejudice, from both critical theory and neuroscience. When discussing the brain in On Intelligence, neuroscientist Jeff Hawkins writes,

“Our brains are always looking at patterns and making analogies. If correct correlations cannot be found, the brain is more than happy to accept false ones. Pseudoscience, bigotry, faith, and intolerance are often rooted in false analogy.” (Hawkins, 2004).

In this second article in a two-part series, I continue to explore how neuroscience and critical theory might help build Ethical AI.

In this series’ first article, regarding critical theory, I argue that intersectionality is important for AI Ethics, data is not objective, and that the structure of language has relevance. Regarding neuroscience, I discussed unconscious bias and introduced the topics of computational neuroscience, synaptic plasticity and symbolic AI, all of which will be explored further here. The purpose of this second part is to theorize a way forward in two steps. First, by using synaptic plasticity to understand learning and mindfulness meditation, and secondly, using critical theory to frame applications of causality and neurosymbolic AI as a means of creating mindful machines.

More specifically,…

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