| Summary: | Outside-in is an installation that utilises machine learning to reflect on systematic discrimination by focusing on the indefinite detention of Mexicans with Japanese heritage concentrated in Morelos during WWII. This algorithmic discrimination system tears apart four classic fiction films continuously within a projection room. The fragments are displaced and classified using machine learning algorithms. The system selects, separates and reassembles the fragments into new orders. It evokes the condition of being robbed of your right to be in the place to which you belong. The citizens detained during WWII were removed from their residence, their belongings were confiscated and they were placed in seclusion solely for having Japanese ancestry. Similarly, at present, data retrieving companies configure low resolution representations of ourselves from the snatched digital debris of our daily life. These pieces are reconfigured into archetypes and meaning is attached to them for massive decision making. We don’t have the right or means to know what these representations look like or what meaning has been attached to such shapes. It is a privilege reserved to the designers of algorithmic processes: they own this right and we the citizens own the consequences. The present article is a case study presenting the creation of Outside in: exile at home as an installation that utilises machine learning and reflects on this kind of systematic discrimination.
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