Bringing the People Back In: Contesting Benchmark Machine Learning Datasets

Abstract

In response to algorithmic unfairness embedded in sociotechnical systems, significant attention has been focused on the contents of machine learning datasets which have revealed biases towards white, cisgender, male, and Western data subjects. In contrast, comparatively less attention has been paid to the histories, values, and norms embedded in such datasets. In this work, we outline a research program - a genealogy of machine learning data - for investigating how and why these datasets have been created, what and whose values influence the choices of data to collect, the contextual and contingent conditions of their creation. We describe the ways in which benchmark datasets in machine learning operate as infrastructure and pose four research questions for these datasets. This interrogation forces us to "bring the people back in" by aiding us in understanding the labor embedded in dataset construction, and thereby presenting new avenues of contestation for other researchers encountering the data.

References in corpus (2)

• Equality of Opportunity in Supervised Learning
• Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls

Cited by in corpus (19)

• Power to the People? Opportunities and Challenges for Participatory AI
• Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
• A survey on GANs for computer vision: Recent research, analysis and taxonomy
• A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
• Data and its (dis)contents: A survey of dataset development and use in machine learning research
• The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
• Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
• Epistemic values in feature importance methods: Lessons from feminist epistemology
• Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?
• Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets
• Subverting machines, fluctuating identities: Re-learning human categorization
• The Limits of Global Inclusion in AI Development
• Model Selection's Disparate Impact in Real-World Deep Learning Applications
• The Benchmark Lottery
• Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers
• In Oxford Handbook on AI Governance: The Role of Workers in AI Ethics and Governance
• On the attribution of confidence to large language models
• Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision
• Nose to Glass: Looking In to Get Beyond