Resource Constraints in the Emergent Community of Machine Learning Practitioners

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Source arXiv

Machine Learning

Computers and Society

Resource Constraints in the Emergent Community of Machine Learning Practitioners

Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development

Machine learning (ml) is increasingly being applied by practitioners from diverse occupations and backgrounds.

However, studies on ml practitioners typically draw populations from big tech and academia, as researchers haveeasier access to these communities.

Through this selection bias, past research often excludes the broader, lesser-resourced, lesser-resourced ml community.

We contribute a qualitative analysis of 17 interviews with stakeholders from organizations which are less represented in previous studies.

We uncover a number of tensions which are introduced or exacerbated by these organizations'resource constraints.

These include tensions between privacy and ubiquity, resource management and performance optimization, and access and monopolization.

Increased academic focus on these practitioners canfacilitate a more holistic understanding of ml limitations, and so is useful for prescribing a research agenda to facilitate responsible ml development for all.

Authors

Aspen Hopkins, Serena Booth

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