The use of machine learning (ML) models in decision-making contexts, particularly those used in high-stakes decision-making, are fraught with issue and peril since a person – not a machine – must ultimately be held accountable for the consequences of decisions made using such systems. Machine learning explainability (MLX) promises to provide decision-makers with prediction-specific rationale, assuring them that the model-elicited predictions are made for the right reasons and are thus reliable. Few works explicitly consider this key human-in-the-loop (HITL) component, however. In this work we propose HEX, a human-in-the-loop deep reinforcement learning approach to MLX. HEX incorporates 0-distrust projection to synthesize decider-specific explainers that produce explanations strictly in terms of a decider’s preferred explanatory features using any classification model. Our formulation explicitly considers the decision boundary of the ML model in question using a proposed explanatory point mode of explanation, thus ensuring explanations are specific to the ML model in question. We empirically evaluate HEX against other competing methods, finding that HEX is competitive with the state-of-the-art and outperforms other methods in human-in-the-loop scenarios. We conduct a randomized, controlled laboratory experiment utilizing actual explanations elicited from both HEX and competing methods. We causally establish that our method increases decider’s trust and tendency to rely on trusted features.

中文翻译：

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HEX：通过深度强化学习实现人机回环可解释性


在决策环境中使用机器学习 （ML） 模型，尤其是那些用于高风险决策的模型，充满了问题和危险，因为最终必须让一个人（而不是机器）对使用此类系统做出的决策的后果负责。机器学习可解释性 （MLX） 承诺为决策者提供特定于预测的基本原理，确保他们模型引发的预测是出于正确的原因，因此是可靠的。然而，很少有作品明确考虑这个关键的人机协同 （HITL） 组件。在这项工作中，我们提出了 HEX，这是一种 MLX 的人机回圈深度强化学习方法。HEX 结合了 0-distrust 投影来合成特定于决策程序的解释器，这些解释器使用任何分类模型严格根据决策程序的首选解释特征生成解释。我们的公式使用建议的解释点解释模式明确考虑了相关 ML 模型的决策边界，从而确保解释特定于相关 ML 模型。我们实证评估了 HEX 与其他竞争方法的对比，发现 HEX 与最先进的方法相比具有竞争力，并且在人机交互场景中优于其他方法。我们利用从 HEX 和竞争方法中得出的实际解释进行随机、对照的实验室实验。我们因果确定我们的方法增加了决策程序的信任度和依赖可信特征的倾向。