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Improving Automation Transparency: Addressing Some of Machine Learning's Unique Challenges

机译：提高自动化透明度：解决一些机器学习的独特挑战

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A variety of factors can affect one's reliance on an automated aid. Some of these factors include one's perception of the system's trustworthiness, such as perceived reliability of the system or one's ability to understand the system's underlying reasoning. A mismatch between the operator's perception and the true capabilities and characteristics of the system can lead to inappropriate reliance on the tool. This improper use of the system can manifest as either underutilization of the technology or complacency resulting from over-trusting the system. Increasing an automated tool's transparency is one approach that enables the operator to more appropriately rely on the technology. Transparent automated systems provide additional information that allows the user to see the system's intent and understand its underlying processes and capabilities. Several researchers have developed frameworks to support the design of more transparent automation. However, these frameworks may not fully consider the particular challenges to transparency design introduced by automation that leverages machine learning. Like all automation, these systems can benefit from transparency. However, artificial intelligence poses new challenges that must be considered when designing for transparency. Unique considerations must be made in terms of the type, and amount or level of transparency information conveyed to the user.
机译：各种因素可以影响一个人对自动援助的依赖。其中一些因素包括一个人对系统可信度的看法，例如感知系统的可靠性，或者一个人理解系统潜在推理的能力。操作员的感知与系统的真实功能和特征之间的不匹配可以导致对工具的不当依赖性。这种不正当使用系统可以表现为未通过过度信任系统的技术或自满来的未充分利用。增加自动化工具的透明度是一种方法，使操作员能够更适当地依赖该技术。透明自动化系统提供额外的信息，允许用户看到系统的意图并理解其底层流程和功能。几位研究人员已经开发了支持更透明的自动化设计的框架。然而，这些框架可能无法完全考虑通过利用机器学习的自动化引入的透明度设计的特殊挑战。与所有自动化一样，这些系统可以从透明度中受益。然而，人工智能在设计透明度时必须考虑的新挑战。必须根据类型的类型和透明信息的量或级别进行唯一的考虑因素。

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