Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development
Open accessAbstract
Practitioners from diverse occupations and backgrounds are increasingly using machine learning (ML) methods. Nonetheless, studies on ML Practitioners typically draw populations from Big Tech and academia, as researchers have easier access to these communities. Through this selection bias, past research often excludes the broader, lesser-resourced ML community---for example, practitioners working at startups, at non-tech companies, and in the public sector. These practitioners share many of the same ML development difficulties and ethical conundrums as their Big Tech counterparts; however, their experiences are subject to additional under-studied challenges stemming from deploying ML with limited resources, increased existential risk, and absent access to in-house research teams. We contribute a qualitative analysis of 17 interviews with stakeholders from organizations which are less represented in prior studies. We uncover a number of tensions which are introduced or exacerbated by these organizations' resource constraints---tensions between privacy and ubiquity, resource management and performance optimization, and access and monopolization. Increased academic focus on these practitioners can facilitate a more holistic understanding of ML limitations, and so is useful for prescribing a research agenda to facilitate responsible ML development for all.
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Practitioners would avoid or limit model updates due to concerns about breaking their models and introducing more failures.
“In another empirical study, Hopkins and Booth [23] found that practitioners would avoid or limit model updates due to concerns about breaking their models and introducing more failures.”
From Improving Human-AI Collaboration With Descriptions of AI Behavior (2023) · cites this paper as Hopkins and Booth [23]
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- Authors
- Hopkins, A., & Booth, S.
- Affiliations
- Massachusetts Institute of Technology (US)
- ORCID
- Booth, Serena
- Venue
- Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2021-07-21
- Pages
- 134-145
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W3190429087
- Cited by
- 40
- References
- 33
- Citations by year
- 2026: 1 · 2025: 11 · 2024: 9 · 2023: 9 · 2022: 10
- FWCI
- 6.33
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Siebel Foundation
- NSF
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- responsible AI development, resource constraints, startups, public sector, selection bias, resource management, performance optimization, existential risk
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