Explabox让机器学习模型更透明，四步分析法可解释、验公平、查安全。
The Explabox: Model-Agnostic Machine Learning Transparency & Analysis
- 四步法：探索、检查、解释、公开，模型无关
- 支持文本数据的性能、行为、公平性等全方位分析
- 开源工具包，适合开发者和测试人员日常使用
我们提出Explabox：一个用于透明化与负责任的机器学习开发与应用的开源工具包。该工具包采用四步策略——探索、检查、解释、公开，实现模型无关的分析，将复杂的模型与数据转化为可理解的可消化内容。其包含描述性统计、性能指标、模型行为解释（局部与全局）、鲁棒性、安全性及公平性评估等模块。基于Python实现，支持多种交互模式，依托开源组件，助力开发者与测试人员落地可解释性、公平性、可审计性与安全性。初始版本聚焦文本数据与模型，未来将扩展。代码与文档已在https://explabox.readthedocs.io/ 开源提供。
原文摘要 · Abstract (English)
We present the Explabox: an open-source toolkit for transparent and responsible machine learning (ML) model development and usage. Explabox aids in achieving explainable, fair and robust models by employing a four-step strategy: explore, examine, explain and expose. These steps offer model-agnostic analyses that transform complex 'ingestibles' (models and data) into interpretable 'digestibles'. The toolkit encompasses digestibles for descriptive statistics, performance metrics, model behavior explanations (local and global), and robustness, security, and fairness assessments. Implemented in Python, Explabox supports multiple interaction modes and builds on open-source packages. It empowers model developers and testers to operationalize explainability, fairness, auditability, and security. The initial release focuses on text data and models, with plans for expansion. Explabox's code and documentation are available open-source at https://explabox.readthedocs.io/.
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