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Cited by 71Ethics and Social Impacts of AIAI and HR TechnologiesDigital Economy and Work Transformation

Whose AI Dream? In search of the aspiration in data annotation.

Open access
Wang, Ding, Prabhat, Shantanu, Sambasivan, Nithya·2022·CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3491102.3502121
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Abstract

Data is fundamental to AI/ML models. This paper investigates the work practices concerning data annotation as performed in the industry, in India. Previous human-centred investigations have largely focused on annotators’ subjectivity, bias and efficiency. We present a wider perspective of the data annotation: following a grounded approach, we conducted three sets of interviews with 25 annotators, 10 industry experts and 12 ML/AI practitioners. Our results show that the work of annotators is dictated by the interests, priorities and values of others above their station. More than technical, we contend that data annotation is a systematic exercise of power through organizational structure and practice. We propose a set of implications for how we can cultivate and encourage better practice to balance the tension between the need for high quality data at low cost and the annotators’ aspiration for well-being, career perspective, and active participation in building the AI dream.

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Crossref · 10.1145/3491102.3502121

Cites

39 of 39 references verified0 citation errors · 0 not checkable

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  • The rise of big data and ML applications has been accompanied by calls for publishing datasets in an open manner, as well as secure access mechanisms for restricted datasets, so that their value can be realised.

    “The rise of big data and ML applications in recent decades has been accompanied by calls for publishing datasets in an open manner, as well as secure access mechanisms for restricted datasets, so that their value can be realised [75].”

    From A Survey of Data Quality Requirements That Matter in ML Development Pipelines (2023) · cites this paper as [75]

  • Big data remain necessary in many ML applications such as autonomous vehicles and clinical diagnosis.

    “early advances in ML were motivated and, in some cases, enabled by the availability of big datasets, and big data remain necessary in many ML applications such as autonomous vehicles and clinical diagnosis [33,75].”

    From A Survey of Data Quality Requirements That Matter in ML Development Pipelines (2023) · cites this paper as [75]

  • Technology companies often outsource data-related tasks through BPO firms or platforms that operate across national borders.

    “When technology companies require data-related tasks to support AI development, they often outsource this work through Business Process Outsourcing (BPO) firms [138] or platforms [4, 67] that typically operate across national borders [23].”

    From 'The plan is just survival': Data Work in Kenya and the Regime of Entrapment (2026) · cites this paper as [138]

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Related

Authors
Wang, D., Prabhat, S., & Sambasivan, N.
Affiliations
Google (United States) (US)
ORCID
Venue
CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2022-04-29
Pages
1-16
DOI
10.1145/3491102.3502121
OpenAlex
W4224981752
Cited by
70
References
38
Citations by year
2026: 19 · 2025: 14 · 2024: 24 · 2023: 12 · 2022: 1
FWCI
12.67
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
Keywords
data annotation, organizational power, work practices, data quality, career aspirations, artificial intelligence industry, human-AI collaboration, organizational structure

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