论文标题
解释性的收益是最优性的损失吗？ - 解释如何偏见决策
Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making
论文作者
论文摘要
组织中的决定是评估替代方案，并选择最能实现组织目标的替代方案。就可以通过适当的指标提出的替代方案评估的范围，机器学习算法越来越多地用于提高流程的效率。解释有助于促进算法与人类决策者之间的沟通，从而使后者更容易根据前者的预测来解释和做出决策。但是，基于特征的解释的因果模型的语义会导致决策者先前的信念泄漏。我们从现场实验的发现表明，这是从经验上表明这如何导致确认偏见和对决策者对预测的信心的不同影响。这种差异会导致次级和偏见的决策结果。
Decisions in organizations are about evaluating alternatives and choosing the one that would best serve organizational goals. To the extent that the evaluation of alternatives could be formulated as a predictive task with appropriate metrics, machine learning algorithms are increasingly being used to improve the efficiency of the process. Explanations help to facilitate communication between the algorithm and the human decision-maker, making it easier for the latter to interpret and make decisions on the basis of predictions by the former. Feature-based explanations' semantics of causal models, however, induce leakage from the decision-maker's prior beliefs. Our findings from a field experiment demonstrate empirically how this leads to confirmation bias and disparate impact on the decision-maker's confidence in the predictions. Such differences can lead to sub-optimal and biased decision outcomes.
