Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness

Abstract

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.

arXiv commentTo appear in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23)

References in corpus (18)

• Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
• HuggingFace's Transformers: State-of-the-art Natural Language Processing
• Equality of Opportunity in Supervised Learning
• Improving fairness in machine learning systems: What do industry practitioners need?
• Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
• Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
• WILDS: A Benchmark of in-the-Wild Distribution Shifts
• Beyond Parity: Fairness Objectives for Collaborative Filtering
• Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
• Training individually fair ML models with Sensitive Subspace Robustness
• How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness
• Detecting race and gender bias in visual representation of AI on web search engines
• Two Simple Ways to Learn Individual Fairness Metrics from Data
• SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
• Auditing ML Models for Individual Bias and Unfairness
• On Measures of Biases and Harms in NLP
• Gerrymandering Individual Fairness
• Towards Involving End-users in Interactive Human-in-the-loop AI Fairness

Cited by in corpus (1)

• Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making