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“Whose intelligence is artificial intelligence?” seems a quite banal question, but to answer such a question it might require quite a complex transdisciplinary journey between the field of critical code
studies, programming, history and philosophy of technology mixed with post-colonial, gender and queer studies. To understand how such a mixture of disciplines might help in answering such a
question it is worth to get it analyzed in its minimum terms. What the question asks is to define from whom the intelligence of artificial intelligence is generated. Of course, a superficial answer would be
by pointing at the various software engineers, but contemporary philosophers and historians of technology seem not so satisfied with such an answer. Current discourses around the genesis of
different configurations of artificial intelligence, might it play go, or recognize cats in pictures, look at how the latter are created by extracting data from the interaction between humans and social media
With such a view in mind it is possible to sketch a new class dimension, the social media platform worker, perhaps. But is then the whole humanity part of this class, or at least the billions of Facebook
and YouTube users? Is anyone affected in the same way by this extractive process? Again, here a superficial answer would be yes, but a deeper look might reveal the inequalities at stake in this
process of intelligence extraction, and it is only with the help of post-colonial, gender and queer studies that it is possible to answer such a question.
One aspect still remains open: how does such an extractive process happen on a technical level? It is at this point that a media practice led investigation is needed. Such investigation will require the
use of networking and debugging tools to understand how platforms are able to congregate the vast amount of information needed to train machine learning algorithms.
Why is it important to understand where the intelligence of AI comes from? The main drive to answer this question is to develop an alternative understanding of what AI systems are. To find new
perspectives that challenge the current technoliberal narrative, that posits machine learning algorithms as what computer scientists E.W. Dijkstra would call it the “philosopher stone”4 of modernity. It is my aim to investigate such topic through the lenses of post-colonial, gender and queer studies, because as sociologist Ruha Benjamin puts it “technology [...] is [...] a metaphor for innovating inequity”5. In the sense that at its root technologies build upon legacies of racial exploitation, like slavery. My contribution to this research will be to address this issue not only from
a theoretical point of view, but to create a dialogue between the discussed theories and practical approach of critical coding.