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Philosophy

Implicit Bias in Artificial Intelligence: the fault is not in our stars…

Image: still from the original movie Blade Runner where a test subject is being examined to determine if it is human or robot. Our androids and cyborgs will be more similar to us than we are willing to accept and for reasons of logic and physics as much as inadvertently coding our own flaws and faults into them.

For all our aspirations to escape the implicit biases and semantic turbulence of cognition, culture and the diverse linguistic or other semi-formal behavioural and conceptual encoding grammars we inhabit, there are very likely intrinsic symmetries in information and communications systems that orient them towards qualititatively dissonant outcomes. Biases are functional amplifications of difference and information itself is always and already a measure – if often abstract – or extrinsic and observable difference.

Bias within gestalt information systems encodes for optimally-concise, energy-efficient and sustainably continuous self-propagation. The distributed presence of dissonant qualities and properties in these systems is itself a function of emergent (but entirely undirected, autonomous) maximal strategies for overall system self-replication and continuity. Dissonance provides recombinatory wiggle-room for the introduction of a threshold level of entropy and uncertainty into linguistic, cognitive and cultural communications systems.

We can not ever effectively counter these biases without dramatically levelling-up the sophistication with which we characterise the holistic information and energy-processing systems they (and we) inhabit.

Image: still from the original movie Blade Runner where a test subject is being examined to determine if it is human or robot. Our androids and cyborgs will be more similar to us than we are willing to accept and for reasons of logic and physics as much as inadvertently coding our own flaws and faults into them.

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