是否信任：机器学习的信念之跃
Whether to trust: the ML leap of faith
AI总结：
针对用户难以信任机器学习系统的问题，提出通过信念之跃矩阵量化模型与专家心智模型的对齐程度，并首次提出基于行为和结果的信任度量，以支持可信赖ML应用。
摘要：
人类信任是可信赖人工智能应用的前提，然而信任仍然未被充分理解。信任常被描述为一种态度，但态度无法被可靠地测量或管理。此外，人类经常混淆对人工智能系统、其机器学习技术及其其他组件的信任。在没有完全理解信任机器学习所涉及的“信念之跃”的情况下，用户无法对这些系统产生内在信任。建立信任的一种常见方法是解释机器学习模型的推理过程。然而，由于机器学习系统固有的复杂性，此类解释往往无法引起非专家的共鸣，且解释与用户自身（未言明的）心智模型脱节。这项工作提出了一种创新方法，通过识别和测量用户在决定依赖机器学习时所采取的信念之跃，直接建立对机器学习的内在信任。信念之跃矩阵捕捉了机器学习模型与人类专家心智模型之间的对齐程度。这种匹配通过将用户的数据和目标函数同时输入机器学习代理和经专家验证的基于规则的代理来严格且实际地识别：这是一个可先验地针对用户自身心智模型进行测试的验证参考点。这代表了一类新的神经符号架构。信念之跃矩阵向用户揭示了构成基于规则代理与机器学习代理之间信念之跃的距离。我们首次提出了信任度量标准，用于评估用户是否通过其行为而非自我报告意图来表现信任，以及这种信任是否基于结果而应得。该贡献的意义在于，它能够对机器学习信任驱动因素进行实证评估和管理，以支持可信赖机器学习的应用。该方法通过一项长期高风险的实地研究加以说明：一个为期3个月的多代理睡眠改善系统试点。
英文摘要：
Human trust is a prerequisite to trustworthy AI adoption, yet trust remains poorly understood. Trust is often described as an attitude, but attitudes cannot be reliably measured or managed. Additionally, humans frequently conflate trust in an AI system, its machine learning (ML) technology, and its other component parts. Without fully understanding the 'leap of faith' involved in trusting ML, users cannot develop intrinsic trust in these systems. A common approach to building trust is to explain a ML model's reasoning process. However, such explanations often fail to resonate with non-experts due to the inherent complexity of ML systems and explanations are disconnected from users' own (unarticulated) mental models. This work puts forward an innovative way of directly building intrinsic trust in ML, by discerning and measuring the Leap of Faith (LoF) taken when a user decides to rely on ML. The LoF matrix captures the alignment between an ML model and a human expert's mental model. This match is rigorously and practically identified by feeding the user's data and objective function into both an ML agent and an expert-validated rules-based agent: a verified point of reference that can be tested a priori against a user's own mental model. This represents a new class of neuro-symbolic architecture. The LoF matrix reveals to the user the distance that constitutes the leap of faith between the rules-based and ML agents. For the first time, we propose trust metrics that evaluate whether users demonstrate trust through their actions rather than self-reported intent and whether such trust is deserved based on outcomes. The significance of the contribution is that it enables empirical assessment and management of ML trust drivers, to support trustworthy ML adoption. The approach is illustrated through a long-term high-stakes field study: a 3-month pilot of a multi-agent sleep-improvement system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
