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

TL;DR

This paper explores how AI startup entrepreneurs navigate institutional pressures, balancing scientific integrity with external expectations, and how these dynamics influence their practices and responses to regulation.

Contribution

It provides empirical insights into organizational and institutional influences on AI startups, highlighting tensions between scientific rigor and external pressures.

Findings

Entrepreneurs value scientific integrity but face external pressures to conform to hype.

External stakeholders often lack technical understanding of AI.

Startups adapt marketing and modeling practices to maintain legitimacy.

Abstract

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.