C.T.R.L. (Culture, Technology, Research, Language)
Machine learning (ML) is a field that uses statistical techniques to give computer systems the ability to "learn" from data and thus improve performance on a specific task without being explicitly programmed. Though this field is growing exponentially and becoming ever more integrated with ubiquitous computing, there exists a lack of representation of diverse groups in the field. This lack of representation in the programing of ML algorithms, collection of datasets, and development of ML based products is deleterious and diminishes the potential positive social impact, efficacy, and economic impact of the field. This lack of diversity additionally has the potential to manifests itself in products and systems that are biased and dangerous to underrepresented groups by not fully taking them into account.
C.T.R.L. is a collaborative endeavor between Nikita R Huggins, Ayodamola Tanimowo Okunseinde, and Nicole Lloyd that seeks to address bias in machine learning systems while creating tools and artworks that make machine learning environments more accessible. The collective not only produces and analyses alternate machine learning datasets, but also create related artworks that are meaningful and expressive. Some of the methodology implemented include identifying unique modes of communication internal to specific communities, analysis of language structures and syntax, the use of machine learning tools to attempt to pull meaning from text, and the creation of physically based works that promote diversity in the machine learning field. The collective aims also to teach machine learning tools and methods to underrepresented communities, develop related art & technology curricula, and to archive assets that may be utilized as research material.
C.T.R.L. projects include Trini Talk, Black Corpus, and Incantation
mediumMachine learning, mixed media