It is widely believed that machine learning can produce useful insights into human behaviours. However, algorithms can also replicate existing racisms and racial hierarchies. On the one hand, an increased awareness of algorithmic bias has heightened the need for new computational practices. On the other, researchers often describe algorithmic bias in ways that might further invalidate Black life. In this way, racialised individuals are seen as “Others”and without agency. Less considered is the affirmative generation of Black life in relation to algorithmic culture. This project asks if it is possible to craft a relationship between Blackness and computation that is something other than the negation of Black life? What might a more affirmative relationship look like? Lastly, can the production of Black life be thought of, following Denise Ferreira da Silva, as a type of “dark matter” or something that but must be experienced prior to being observed?

The project takes a unique approach to these questions by using machine learning methods to capture discrete fragments of Black experience in single, non-repeatable, moments of public performance, group exhibition, peer-reviewed text, and contributions to the field by means of consultation and advisory. Like machine learning, the project uses these traces as data to inform the next iterative capture of racialised experience. In this way, each item of the project contributes both to a single understanding of the conditions of Black life from within computational systems, as well as to a larger comprehension of the substances of race in the wider techno-racial environment.

The project has been disseminated widely at non academic and academic institutions, including the XXII Triennale di Milano (Italy), the Barbican (London), Royal Academy of Art (The Hague), Het Nieuwe Instituut (Rotterdam), Critical Curatorial Cybernetic Studies (Geneva), Sonic Acts (Berlin), and Yale School of Art (USA).

http://research.gold.ac.uk/id/eprint/27320