seeBias工具可全面评估AI模型公平性，发现传统方法遗漏的偏差。
seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness
- 整合分类、校准等多维度评估模型公平性
- 在司法与医疗数据中揭示了传统指标忽略的不公平现象
- 提供可定制可视化，适合负责任AI开发团队使用
人工智能预测模型的公平性日益受到重视，尤其在医疗和司法等高风险领域。尽管指南强调预测准确性和公平结果并重，现有公平性工具包通常仅孤立评估分类性能差异，忽视校准等关键方面。为此，我们提出seeBias，一个R语言包，实现对模型公平性和预测性能的综合评估。该工具涵盖分类、校准及其他性能维度，提供更全面的模型行为视图，并支持透明报告与负责任的AI实施。通过刑事司法和医疗领域的公开数据集，我们展示了seeBias如何识别出传统公平性度量可能遗漏的差异。R版本已在GitHub开源，Python版本正在开发中。
原文摘要 · Abstract (English)
Fairness in artificial intelligence (AI) prediction models is increasingly emphasized to support responsible adoption in high-stakes domains such as health care and criminal justice. Guidelines and implementation frameworks highlight the importance of both predictive accuracy and equitable outcomes. However, current fairness toolkits often evaluate classification performance disparities in isolation, with limited attention to other critical aspects such as calibration. To address these gaps, we present seeBias, an R package for comprehensive evaluation of model fairness and predictive performance. seeBias offers an integrated evaluation across classification, calibration, and other performance domains, providing a more complete view of model behavior. It includes customizable visualizations to support transparent reporting and responsible AI implementation. Using public datasets from criminal justice and healthcare, we demonstrate how seeBias supports fairness evaluations, and uncovers disparities that conventional fairness metrics may overlook. The R package is available on GitHub, and a Python version is under development.
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