Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often based on more complex algorithms and therefore lack explainability and vice versa. However, there is little to no empirical evidence of this tradeoff from an end user perspective. We aim to provide empirical evidence by conducting two user experiments. Using two distinct datasets, we first measure the tradeoff for five common classes of machine learning algorithms. Second, we address the problem of end user perceptions of explainable artificial intelligence augmentations aimed at increasing the understanding of the decision logic of high-performing complex models. Our results diverge from the widespread assumption of a tradeoff curve and indicate that the tradeoff between model performance and explainability is much less gradual in the end user's perception. This is a stark contrast to assumed inherent model interpretability. Further, we found the tradeoff to be situational for example due to data complexity. Results of our second experiment show that while explainable artificial intelligence augmentations can be used to increase explainability, the type of explanation plays an essential role in end user perception.
翻译:机器学习算法能够让当代智能系统作出先进的决策。 研究表明,模型性能和解释性之间存在着一种权衡。 高性能的机器学习模型往往基于更复杂的算法,因此缺乏解释性,反之亦然。 但是,从终端用户的角度,这种权衡的实证证据很少,甚至根本没有。 我们的目标是通过进行两个用户实验来提供经验证据。 我们首先用两个不同的数据集来衡量五类通用机器学习算法的权衡。 其次, 我们处理终端用户对可解释的人工智能增强的认知问题,目的是增加对高性能复杂模型决策逻辑的理解。 我们的结果不同于对交易曲线的广泛假设,并指出模型性与解释性之间的权衡在终端用户的认知中不是那么渐进的。 这是与假定固有的模型解释性的鲜明对比。 此外,我们发现这种权衡是因数据的复杂性而出现的情况。 我们的第二次实验的结果表明,虽然可以解释的人工智能增强可以用来增加解释性,但解释性的解释类型在终端用户的认知中起着必不可少的作用。