Document Type

Article

Publication Date

2022

Publication Citation

60 Duquesne Law Review 271

Abstract

This Article uses a wrongful conviction lens to compare identifications by machines, notably facial recognition software, with identifications by humans. The Article advocates for greater reliability checks on both before use against a criminal defendant. The Article examines the cascading influence of facial recognition software on eyewitness identifications themselves and the related potential for greater errors. As a solution, the Article advocates the inclusion of eyewitness identification in the Organization of Scientific Area Committees' ("OSAC") review of facial recognition software for a more robust examination and consideration of software and its usage. The Article also encourages police departments to adopt double- blind procedures for eyewitness identifications, including when "matching" photos from facial recognition software are included. Finally, the Article concludes with a prediction of where these two fields will be ten years from now, in 2032.

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