- Project:C.T.R.L. Alliance
- Year: 2018... ongoing
- Team: Ayodamola Tanimowo Okunseinde, Nikita R Huggins, Nicole Lloyd
Concept, Design, Technology
Machine learning (ML) uses statistical techniques to give computers the ability to "learn" from data and thus improve performance on a specific task without being explicitly programmed. Though this field is growing exponentially, 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. seeks to address bias in machine learning systems while creating tools and artworks that make machine learning environments more accessible. 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.