Recent Award

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. This study explores the limitations of such machine learning systems by tracing how technical and non-technical people, including funding agencies, have historically understood what machine learning systems could and should achieve. Particular care is given to the forms of "learning" valued by researchers during different moments in the 20th century, and to the emergence of theoretical concepts that were constrained and even defined by the capabilities of the available material devices. This work makes visible the efforts of women and men previously omitted in histories of artificial intelligence and machine learning, and develops a quantitative method to document how the innovations of a discipline are contingent upon interdisciplinary and transdisciplinary research networks. Finally, this project traces how the allocation of resources to particular research communities spurred scientific innovation in adjacent and seemingly unrelated academic research fields in the physical and social sciences. In this sense, the discipline of machine learning provides a useful case study for modeling the propagation of ideas across different subfields.