我们是否学会了解释：解释性方法如何学习在解释中编码预测
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
摘要：
虽然已经建立了对可解释的机器学习的需求，但许多常见的方法都很慢，缺乏保真度或难以评估。分期解释方法通过学习全局选择器模型来降低提供解释的成本，该模型返回单个数据实例的特征重要性。选择器模型被训练以优化解释的保真度，如由目标的预测器模型评估的。流行的方法学习选择器和预测器模型，我们表明这允许预测在解释中编码。我们引入EVAL-X作为一种定量评估解释的方法，REAL-X作为一种摊销解释方法，它学习一个预测模型，该模型近似于给定输入的任何子集的真实数据生成分布。我们表明，EVAL-X可以检测预测何时被编码在解释中，并通过定量和放射科医生评估显示REAL-X的优势。
英文摘要：
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce the cost of providing interpretations by learning a global selector model that returns feature importances for a single instance of data. The selector model is trained to optimize the fidelity of the interpretations, as evaluated by a predictor model for the target. Popular methods learn the selector and predictor model in concert, which we show allows predictions to be encoded within interpretations. We introduce EVAL-X as a method to quantitatively evaluate interpretations and REAL-X as an amortized explanation method, which learn a predictor model that approximates the true data generating distribution given any subset of the input. We show EVAL-X can detect when predictions are encoded in interpretations and show the advantages of REAL-X through quantitative and radiologist evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
