AIDRIN 2.0 检测数据质量与隐私，提升AI训练准备度
AIDRIN 2.0: A Framework to Assess Data Readiness for AI
- 优化界面并集成隐私保护联邦学习框架
- 实证发现数据问题显著影响模型性能
- 适合关注数据合规与模型可信的开发者
AI数据就绪检查器（AIDRIN）是一个评估和提升人工智能应用数据准备度的框架，涵盖数据质量、偏见、公平性和隐私等关键维度。本文重点改进了AIDRIN的用户界面，并实现了与隐私保护联邦学习（PPFL）框架的集成。通过优化交互体验和打通去中心化AI流程，AIDRIN更易于不同技术水平的用户使用。集成现有PPFL框架确保在联邦学习环境中同步保障数据就绪性与隐私安全。一项基于真实数据集的案例研究验证了AIDRIN在识别影响模型表现的数据问题方面的实际价值。
原文摘要 · Abstract (English)
AI Data Readiness Inspector (AIDRIN) is a framework to evaluate and improve data preparedness for AI applications. It addresses critical data readiness dimensions such as data quality, bias, fairness, and privacy. This paper details enhancements to AIDRIN by focusing on user interface improvements and integration with a privacy-preserving federated learning (PPFL) framework. By refining the UI and enabling smooth integration with decentralized AI pipelines, AIDRIN becomes more accessible and practical for users with varying technical expertise. Integrating with an existing PPFL framework ensures that data readiness and privacy are prioritized in federated learning environments. A case study involving a real-world dataset demonstrates AIDRIN's practical value in identifying data readiness issues that impact AI model performance.
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