Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?

Abstract

Many machine learning projects for new application areas involve teams of humans who label data for a particular purpose, from hiring crowdworkers to the paper's authors labeling the data themselves. Such a task is quite similar to (or a form of) structured content analysis, which is a longstanding methodology in the social sciences and humanities, with many established best practices. In this paper, we investigate to what extent a sample of machine learning application papers in social computing --- specifically papers from ArXiv and traditional publications performing an ML classification task on Twitter data --- give specific details about whether such best practices were followed. Our team conducted multiple rounds of structured content analysis of each paper, making determinations such as: Does the paper report who the labelers were, what their qualifications were, whether they independently labeled the same items, whether inter-rater reliability metrics were disclosed, what level of training and/or instructions were given to labelers, whether compensation for crowdworkers is disclosed, and if the training data is publicly available. We find a wide divergence in whether such practices were followed and documented. Much of machine learning research and education focuses on what is done once a "gold standard" of training data is available, but we discuss issues around the equally-important aspect of whether such data is reliable in the first place.

arXiv comment18 pages, includes appendix

References in corpus (8)

• The NumPy array: a structure for efficient numerical computation
• Model Cards for Model Reporting
• 10 Simple Rules for the Care and Feeding of Scientific Data
• Multilingual Twitter Sentiment Classification: The Role of Human Annotators
• The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards
• Decision Provenance: Harnessing data flow for accountable systems
• ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles
• Towards Traceability in Data Ecosystems using a Bill of Materials Model

Cited by in corpus (5)

• CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation
• Machine Learning (In) Security: A Stream of Problems
• Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda
• Towards a multi-stakeholder value-based assessment framework for algorithmic systems
• Algorithmic Fairness Datasets: the Story so Far