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SUMMARY:Epistemological Concerns of Missing Data - Chelsea Schwartz
DTSTART;TZID=US/Central:20260715T164000
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DTSTAMP:20260726T081904Z
UID:pretalx-iacap-2026-LE9H8F@pretalx.iacapconf.org
DESCRIPTION:This paper explores the epistemological repercussions associat
 ed with incomplete datasets in machine learning and subsequent artificial 
 intelligence applications. Extensive literature has emerged in the last de
 cade showing how data-driven computing technologies\, such as machine lear
 ning\, tend to reproduce structural inequalities due\, in part\, to biased
  datasets used in their training. This bias and its implications have ofte
 n been explored from the analysis of constituent items in data sets—for 
 example\, the predominant representation of one group over another in trai
 ning and benchmark datasets. This paper explores a similar yet distinct is
 sue in dataset curation at the center of machine learning technologies: th
 e implications of missing data. In particular\, this paper focuses on how 
 such omission may fail to encode significant cultural values into the comp
 uting architecture of AI and considers the epistemological challenges this
  poses\, referencing what Sabina Leonelli calls data imaginaries (2021). T
 hrough this analysis\, this paper argues that missing data is not simply a
  technical failure or oversight but an ethical and epistemological problem
  to be addressed. This paper helps elucidate and articulate how examining 
 where and why missing data leads to unique forms of cultural loss perpetua
 ted—or at least uniquely exacerbates through scale\, prowess\, and speed
 —data-driven computing technologies\, such as machine learning\, as they
  occupy more and more social spaces.
LOCATION:Executive Conference Room
URL:https://pretalx.iacapconf.org/iacap-2026/talk/LE9H8F/
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