Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
This work addresses the problem of understanding practitioner strategies for fairness evaluation in text classification, which is incremental as it builds on existing fairness toolkits by exploring real-world assessment methods.
The study investigated how machine learning practitioners evaluate fairness in text classification models, finding that the type of fairness metric (group vs. individual) influences their assessments, with participants focusing on risks like underpredicting and sensitivity to identity token manipulations.
Mitigating algorithmic bias is a critical task in the development and deployment of machine learning models. While several toolkits exist to aid machine learning practitioners in addressing fairness issues, little is known about the strategies practitioners employ to evaluate model fairness and what factors influence their assessment, particularly in the context of text classification. Two common approaches of evaluating the fairness of a model are group fairness and individual fairness. We run a study with Machine Learning practitioners (n=24) to understand the strategies used to evaluate models. Metrics presented to practitioners (group vs. individual fairness) impact which models they consider fair. Participants focused on risks associated with underpredicting/overpredicting and model sensitivity relative to identity token manipulations. We discover fairness assessment strategies involving personal experiences or how users form groups of identity tokens to test model fairness. We provide recommendations for interactive tools for evaluating fairness in text classification.