There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms. Providing an explanation of the reasoning process in domain-specific terms can be crucial for adoption in risk-sensitive domains such as healthcare. We argue that machine learning algorithms should be interpretable by design and that the language in which these interpretations are expressed should be domain- and task-dependent. Consequently, we base our model's prediction on a family of user-defined and task-specific binary functions of the data, each having a clear interpretation to the end-user. We then minimize the expected number of queries needed for accurate prediction on any given input. As the solution is generally intractable, following prior work, we choose the queries sequentially based on information gain. However, in contrast to previous work, we need not assume the queries are conditionally independent. Instead, we leverage a stochastic generative model (VAE) and an MCMC algorithm (Unadjusted Langevin) to select the most informative query about the input based on previous query-answers. This enables the online determination of a query chain of whatever depth is required to resolve prediction ambiguities. Finally, experiments on vision and NLP tasks demonstrate the efficacy of our approach and its superiority over post-hoc explanations.
翻译:人们日益关注以高性能机器学习算法进行通常不透明的决策,这种典型的不透明决策,以高性能的机器学习算法为高性能机器学习算法。对特定领域的推理过程作出解释,对于在诸如医疗保健等风险敏感领域采用,可能是至关重要的。我们主张,机器学习算法应当通过设计来解释,而解释这些解释所使用的语言应当以领域和任务为依托。因此,我们模型的预测基于数据用户定义和具体任务二元功能的大家庭,每个模型都对最终用户有清楚的解释。然后,我们尽可能减少准确预测任何特定投入所需的查询的预期数量。由于解决办法一般难以解决,我们根据信息收益按顺序选择查询。然而,与以往的工作不同,我们不需要假定这些查询是有条件的。相反,我们利用一种随机的组合模型(VAE)和MC算法(Unaddaddad Langevin)来选择关于输入内容的最丰富的查询。这样就可以在网上确定任何深度的查询链,从而解决预测的模糊性。最后,关于愿景的实验和NLP的任务是超越我们的优越性的解释方法。