“Re-writing Algorithms for Just Recognition: from Digital Aural Redlining to Accent Activism”

Prof. Nina Eidsheim, University of California, Los Angeles

Abstract

In the mid-1950s, the Eastman Kodak Company famously standardized their Shirley cards test, which used a photograph of a white woman to calibrate color when printing photos. Jersson Garcia, who worked at a photo lab told the NPR: “‘She was the standard,’ ‘so whenever we printed anything, we had to pull Shirley in. If Shirley looked good, everything else was OK. If Shirley didn't look so hot that day, we had to tweak something — something was wrong.’”[i] Around the Christmas shopping season of 2009, we saw the same premise illustrated by YouTube videos featuring Hewlett Packard webcams that “can’t see black people.” In this chapter, I argue that, in the same way as Kodak film and HP cameras were calibrated for white skin color, voice- and listening technologies also carry and reproduce the same social bias, discrimination, and racism. Akin to discriminatory real estate and lending-practice redlining, I dub these practices digital aural redlining. I also identify oppositional uses of these technologies that could counter essentializing practices as aural redline jamming. Considering the vocal synthesis software Vocaloid, voice to text technology, and the Voice Bank Monopoly game, I show how vocal and listening technology listens for, against, and in non-recognition of certain accents, vocal performances standing in for non-whiteness. I conclude by calling for each (accented) accent to be justly recognized, affording each voice its multiplicity and humanity.