走向机器引导的、人类发起的解释性交互学习
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning
摘要：
最近的工作已经证明了将局部解释与主动学习相结合来理解和监督黑盒模型的前景。在这里，我们表明，在特定条件下，这些算法可能会歪曲正在学习的模型的质量。原因是机器通过预测和解释查询实例的标签来说明它的信念：如果机器不知道自己的错误，它可能最终选择它人为表现良好的查询。我们通过引入解释性引导学习来解决这种叙事偏见，解释性引导学习是一种新颖的交互式学习策略，其中：i）监督者负责选择查询实例，而ii）机器使用全局解释来说明其整体行为，并指导监督者选择具有挑战性的，信息丰富的实例。这种策略保留了解释性互动的关键优势，同时避免了叙事偏见，并在样本复杂性方面优于主动学习。基于聚类的原型的初步实证评估突出了我们的方法的承诺。
英文摘要：
Recent work has demonstrated the promise of combining local explanations with active learning for understanding and supervising black-box models. Here we show that, under specific conditions, these algorithms may misrepresent the quality of the model being learned. The reason is that the machine illustrates its beliefs by predicting and explaining the labels of the query instances: if the machine is unaware of its own mistakes, it may end up choosing queries on which it performs artificially well. This biases the "narrative" presented by the machine to the user.We address this narrative bias by introducing explanatory guided learning, a novel interactive learning strategy in which: i) the supervisor is in charge of choosing the query instances, while ii) the machine uses global explanations to illustrate its overall behavior and to guide the supervisor toward choosing challenging, informative instances. This strategy retains the key advantages of explanatory interaction while avoiding narrative bias and compares favorably to active learning in terms of sample complexity. An initial empirical evaluation with a clustering-based prototype highlights the promise of our approach.
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。
