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

Abstract

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.

References in corpus (4)

• Model Cards for Model Reporting
• Improving fairness in machine learning systems: What do industry practitioners need?
• Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
• Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs

Cited by in corpus (11)

• Improving Human-AI Collaboration With Descriptions of AI Behavior
• Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning
• Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability
• Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups
• Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
• XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development
• Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts
• Machine Learning practices and infrastructures
• Towards Standardized Mobility Reports with User-Level Privacy
• Collection, usage and privacy of mobility data in the enterprise and public administrations
• "Is It My Turn?" Assessing Teamwork and Taskwork in Collaborative Immersive Analytics