用置信度可保证的预测提升AI可靠性，助力可信AI发展
Conformal Prediction and Trustworthy AI
- 基于置信集提供可验证的不确定性估计
- 实验证明其能准确识别并缓解模型偏差
- 适合关注AI安全与治理的研究者与工程师
conformal prediction 是20世纪90年代由Gammerman、Vovk等人提出的一种机器学习算法，可生成具有保证置信水平的集合预测。近年来，该方法在机器学习领域广泛应用，成为不确定性量化的主要手段之一。自诞生起即具备可靠校准的不确定性估计能力，对构建可信AI至关重要。本文综述了conformal prediction在超越边际有效性之外的潜力，涵盖泛化风险控制与AI治理等关键问题，并通过实验与案例展示其作为校准良好预测器的应用价值，以及在偏差识别与缓解中的作用。
原文摘要 · Abstract (English)
Conformal predictors are machine learning algorithms developed in the 1990's by Gammerman, Vovk, and their research team, to provide set predictions with guaranteed confidence level. Over recent years, they have grown in popularity and have become a mainstream methodology for uncertainty quantification in the machine learning community. From its beginning, there was an understanding that they enable reliable machine learning with well-calibrated uncertainty quantification. This makes them extremely beneficial for developing trustworthy AI, a topic that has also risen in interest over the past few years, in both the AI community and society more widely. In this article, we review the potential for conformal prediction to contribute to trustworthy AI beyond its marginal validity property, addressing problems such as generalization risk and AI governance. Experiments and examples are also provided to demonstrate its use as a well-calibrated predictor and for bias identification and mitigation.
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