Applying a critical theory framework to AI Ethics, while using neuroscience to understand unconscious bias with synaptic plasticity.

A year ago when discussing racial bias present in facial recognition, AI pioneer Yan Lecun controversially tweeted, “ML systems are biased when data is biased” (source: Twitter). This provoked a response from AI Ethics researcher, Timnit Gebru, who expressed her frustration at the overly simplistic framing of this issue, an opinion based on her expertise in AI Ethics (source: Twitter). Gebru’s reply and the ensuing conversation was amplified by mainstream media, and while this did prompt a broader discussion of bias in the AI community, the media focus was on how Lecun chose to (mis)communicate.

Irrespective of sincerity, Lecun’s apology (source) reminded me of Audre Lorde’s thoughts on guilt,

“all too often, it becomes a device to protect ignorance and the continuation of things the way they are, the ultimate protection for changelessness.” (Lorde, 1981).

And with regards to the entitled expectation that the marginalized should be responsible for educating others about bias, Lorde says,

“[t]here is a constant drain of energy which might be better used in redefining ourselves and devising realistic scenarios for altering the present and constructing the future.” (Lorde, 1980).

I share Gebru’s frustration. Societal bias pervades every aspect of AI, from the datasets, the research environments, to even the practitioners themselves. An equitable future depends on our ability to create Ethical AI; hence, I believe it is important to reflect critically on bias — despite how emotionally difficult this can be, and despite the lack of easy answers. Moreover, discussing bias without addressing social and structural problems is a hollow and ultimately, meaningless endeavour. Consequently, I seek to integrate critical theory with neuroscience to understand unconscious bias and chart a path towards Ethical AI.

In a two-part series, I draw from diverse fields such as neuroscience, genetics, psychology, critical theory, linguistics, mathematics and pedagogy, to articulate my view that AI Ethics can benefit from an unconventional approach that combines various disciplines. This first article uses neuroscience to understand unconscious bias while making direct connections to AI, all within a critical theory framework. The second article explores learning with respect to neuroscience and…

Continue reading: https://towardsdatascience.com/understanding-bias-neuroscience-critical-theory-for-ethical-ai-de7a31db6c05?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com