论文标题
从（DIS）类似机器中获取建议：人机相似性对机器辅助决策的影响
Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making
论文作者
论文摘要
机器学习算法越来越多地用于协助人类决策。当机器援助的目标是提高人类决策的准确性时，似乎很吸引人设计补充人类知识的ML算法。尽管算法和人都不是完全准确的，但人们可以期望他们的互补专业知识可能会改善结果。在这项研究中，我们证明，在实践中，不是互补的，但与人类类似的错误可能会有自己的利益。 在一系列具有901名参与者的人类受试者实验中，我们研究人类和机器错误的相似性如何影响人类对算法决策艾滋病的看法和相互作用。我们发现（i）人们认为更相似的决策辅助工具是更有用，准确和可预测的，并且（ii）人们更有可能从更相似的决策辅助工具那里获得相反的建议，而（iii）与人类不太相似的决策辅助工具有更多机会提供对立的建议，从而对人们的决策产生更大的影响。
Machine learning algorithms are increasingly used to assist human decision-making. When the goal of machine assistance is to improve the accuracy of human decisions, it might seem appealing to design ML algorithms that complement human knowledge. While neither the algorithm nor the human are perfectly accurate, one could expect that their complementary expertise might lead to improved outcomes. In this study, we demonstrate that in practice decision aids that are not complementary, but make errors similar to human ones may have their own benefits. In a series of human-subject experiments with a total of 901 participants, we study how the similarity of human and machine errors influences human perceptions of and interactions with algorithmic decision aids. We find that (i) people perceive more similar decision aids as more useful, accurate, and predictable, and that (ii) people are more likely to take opposing advice from more similar decision aids, while (iii) decision aids that are less similar to humans have more opportunities to provide opposing advice, resulting in a higher influence on people's decisions overall.
