DetoxAI是一个用于视觉模型去偏的开源工具包，让算法更公平。
DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision
- 通过干预内部表征实现视觉模型的去偏
- 集成先进去偏算法与可量化公平性指标
- 适合关注算法公平性的研究者与工程师
尽管机器学习公平性近年取得进展，但多数方法聚焦于表格数据，不适用于依赖深度学习的视觉分类任务。为此，我们提出DetoxAI，一个开源Python工具包，旨在通过后处理去偏提升深度学习视觉分类器的公平性。DetoxAI实现了前沿的去偏算法、公平性度量和可视化工具，支持在内部表示中进行干预，并提供基于归因的可视化与定量公平性指标，以展示偏见缓解效果。本文阐述了DetoxAI的设计动机、架构及使用案例，验证其对研究人员与工程师的实际价值。
原文摘要 · Abstract (English)
While machine learning fairness has made significant progress in recent years, most existing solutions focus on tabular data and are poorly suited for vision-based classification tasks, which rely heavily on deep learning. To bridge this gap, we introduce DetoxAI, an open-source Python library for improving fairness in deep learning vision classifiers through post-hoc debiasing. DetoxAI implements state-of-the-art debiasing algorithms, fairness metrics, and visualization tools. It supports debiasing via interventions in internal representations and includes attribution-based visualization tools and quantitative algorithmic fairness metrics to show how bias is mitigated. This paper presents the motivation, design, and use cases of DetoxAI, demonstrating its tangible value to engineers and researchers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。