Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework

Abstract

Studies of dataset development in machine learning call for greater attention to the data practices that make model development possible and shape its outcomes. Many argue that the adoption of theory and practices from archives and data curation fields can support greater fairness, accountability, transparency, and more ethical machine learning. In response, this paper examines data practices in machine learning dataset development through the lens of data curation. We evaluate data practices in machine learning as data curation practices. To do so, we develop a framework for evaluating machine learning datasets using data curation concepts and principles through a rubric. Through a mixed-methods analysis of evaluation results for 25 ML datasets, we study the feasibility of data curation principles to be adopted for machine learning data work in practice and explore how data curation is currently performed. We find that researchers in machine learning, which often emphasizes model development, struggle to apply standard data curation principles. Our findings illustrate difficulties at the intersection of these fields, such as evaluating dimensions that have shared terms in both fields but non-shared meanings, a high degree of interpretative flexibility in adapting concepts without prescriptive restrictions, obstacles in limiting the depth of data curation expertise needed to apply the rubric, and challenges in scoping the extent of documentation dataset creators are responsible for. We propose ways to address these challenges and develop an overall framework for evaluation that outlines how data curation concepts and methods can inform machine learning data practices.

In ACM Conference on Fairness, Accountability, and Transparency 2024. ACM, Rio de Janeiro, Brazil

References in corpus (7)

• Improving fairness in machine learning systems: What do industry practitioners need?
• Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches
• Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
• Problem Formulation and Fairness
• Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
• Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline
• Algorithms as Social-Ecological-Technological Systems: an Environmental Justice Lens on Algorithmic Audits