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Accepted Paper:

Uncovering facial data annotation  
Assia Wirth

Short abstract:

This paper examines the role of freelance platforms in current production of face analysis (FA) technologies. Building on qualitative research conducted with freelance platform workers, this paper argues that more often than not, these workers’ expertise becomes invisibilized during FA production.

Long abstract:

This paper examines the role of freelance platforms in current production of face analysis (FA) technologies. FA is developed by first collecting and annotating vast volumes of data, and subsequently designing models which may be trained upon these datasets. This work has been increasingly outsourced through complex labor networks. These neocolonial dynamics are often framed by ML producers in the global North as a matter of highly skilled engineers delegating the more menial work to low skilled workers, the latter’s contribution often remaining unacknowledged. This paper seeks to present a counter narrative, where the ‘high V. low skilled’ framing only makes sense as an attempt to rewrite and obscure the coloniality of the digital labor industry. Building on qualitative research conducted with freelance platform workers based in Kenya and Uganda, this paper argues that more often than not, these workers’ expertise becomes invisibilized during FA production. Yet what sets platform workers apart from their clients is not their abilities but rather the nature of this work and their geographical and socioeconomic context. The latter, shaped by complex colonial histories, benefits ML industries in the global North, which are able through the freelance platforms to access seamlessly services catering to their needs. Thus, freelancing platforms stand out as a new site of extractivist production for the ML industry, that enables the latter to exploit asymmetric dynamics established through colonisation, reaffirming “violence at scale” (Ricaurte 2021) as the modus operandi of global ML and FA production.

Traditional Open Panel P348
Digital ghost work: human presences in AI transformations
  Session 2 Tuesday 16 July, 2024, -