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Self-Diagnosing Systems. Standardized tests as self-replicating system.
Taking common multiple choice tests from the fields of educational achievement and psychological diagnosis, this project explores the forms of those tests and the psychology of human viewers/participants. I envision this as a reciprocal investigation: elucidating the formal structure, descriptive limitations, and values embedded in a variety of testing metrics (including psychological inventories, diagnostic tools, intelligence tests, and scholastic aptitude / achievement tests) while also engaging the viewer/user in some reflexive consideration of their own psychology in relation to that test. These To the extent that assessment tests hold great weight are used in determining future outcomes of people in society and as thus for individuals I think they merit further critical analysis.
The central interactive model for this project is the idea of the user administering tests to the computer--an exchange suggesting the need to diagnose the computer (and through it, the test) to discover its underlying pathology, and prompting the viewer to form some sense of the parameters and limitations of testing tools. Multiple-choice tests are closed systems with finite possible outcomes and descriptive states--and as such they are already essentailly "machines" for producing diagnoses, and thus are ripe for interface and exploitation by statistical or learning machines. Computer vision (CV) and statistical machine learning technologies are the means to orchestrate this encounter between the viewer and the test and facilitate the ongoing interaction between the two.