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Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI StartupsArtificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups

  • Paper
  • #ArtificialIntelligence #EnterpriseTech
Amy Winecoff
@aawinecoff
(Author)
Elizabeth Anne Watkins, PhD
@watkins_welcome
(Author)
arxiv.org
Read on arxiv.org
1 Recommender
2 Mentions
Scholars and industry practitioners have debated how to best develop interventions for ethical artificial intelligence (AI). Such interventions recommend that companies building and... Show More

Scholars and industry practitioners have debated how to best develop interventions for ethical artificial intelligence (AI). Such interventions recommend that companies building and using AI tools
change their technical practices, but fail to wrangle with critical questions about the organizational and institutional context in which AI is developed. In this paper, we contribute descriptive research around the life of ”AI” as a discursive concept and organizational practice in an understudied sphere–emerging AI startups–and with a focus on extra-organizational pressures faced by entrepreneurs. Leveraging a theoretical lens for how organizations change, we conducted semi-structured interviews with 23
entrepreneurs working at early-stage AI startups. We find that actors within startups both conform to and resist institutional pressures. Our analysis identifies a central tension for AI entrepreneurs:
they often valued scientific integrity and methodological rigor; however, influential external stakeholders either lacked the technical knowledge to appreciate entrepreneurs’ emphasis on rigor or
were more focused on business priorities. As a result, entrepreneurs adopted hyped marketing messages about AI that diverged from their scientific values, but attempted to preserve their legitimacy internally. Institutional pressures and organizational constraints also
influenced entrepreneurs’ modeling practices and their response to actual or impending regulation. We conclude with a discussion for how such pressures could be used as leverage for effective interventions towards building ethical AI.

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Arvind Narayanan @random_walker · Jul 5, 2022
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It's not just journalists who don't understand ML accuracy measurement. In a brilliant paper, @aawinecoff & @watkins_welcome explain how VCs have extremely superficial ideas about accuracy and how startup founders respond to these misinformed expectations:
Arvind Narayanan @random_walker · Feb 17, 2023
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Exactly — the reason buzzwords are so persistent is that it's a collective action problem. Everyone hates it yet feels compelled to use it. As explained in a brilliant paper by @aawinecoff and @watkins_welcome:
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