This doctoral dissertation research project is a study of vision that traces the development of ophthalmology in early modern Europe. The research will use archival sources and historical analysis to investigate the ways in which the eye was studied, eye diseases were treated, and the knowledge of the eye was transmitted during the sixteenth and seventeenth centuries in Europe. Knowledge of the eye not only formed a critical branch of medical and technological investigation, it was also of cultural and scientific significance.
Machine learning techniques currently make "high-stakes" judgments in areas as diverse as criminal justice, credit risk, social welfare, hiring, and congressional redistricting. Such techniques make these decisions using patterns learned from historical social data. Emphasis on prediction rather than the circumstances of dataset creation have led to machine learning systems that preferentially target vulnerable populations for disparately adverse social judgments while making it more difficult for those subject to these decisions to protest unfair treatment.
The FOIArchive makes it possible to explore millions of declassified government records. The project aggregates collections that are currently scattered across virtual archives, extracts unique metadata, and makes all of it available through web-based interactive tools. This extension also will make these data and tools compatible with Columbia Library systems and software. Users worldwide will be able to get an aggregate view of entire archives, filter subsets of metadata, and see the specific words that produced a single data point.
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