AI 中文总结
本论文研究设计人机交互的方法，以帮助用户形成对AI系统的准确信念、鼓励改进并抑制投机、保障AI系统达成预期目标，从而推进以人为本的机器学习。
AI 中文摘要
当AI系统被部署时，使用它或接受其评估的个人会形成关于该系统运行方式的信念，并利用这些信念战略性地呈现自身的偏好、行为或属性；随后系统会以反馈或决策结果作为回应，由此形成人机交互循环。本论文研究如何设计和塑造这类交互以实现三个目标：其一，帮助个人形成关于AI系统的准确信念，使其能以最小成本改进自身或获得有利结果；其二，鼓励改进行为或抑制投机取巧的行为；其三，确保AI系统持续实现其预期目标，例如最大化准确率。为达成这些目标，本论文分为三个互补部分，分别从被评估个人和AI系统的视角研究人机交互。总体而言，本论文的研究工作通过提供设计AI系统的原则与方法，推进了以人为本的机器学习，这类系统需与人类的需求、价值观和能力相契合。在方法论上，本论文整合了理论分析、数据驱动建模、人类主体实验，以及在真实世界和半合成数据集上的实证评估。
英文摘要
When an AI system is deployed, the individuals who use and or are evaluated by it form beliefs about how the system operates and use those beliefs to strategically present their preferences, behaviors, or attributes. The system then responds with feedback or a decision outcome, thereby creating a human-AI interaction loop. This thesis studies how to design and shape such interactions to achieve three goals: (1) help individuals develop accurate beliefs about the AI systems so they can improve and or secure favorable outcomes at minimal cost, (2) encourage improvement and or discourage gaming behaviors, and (3) ensure that the AI system continues to achieve its intended objectives, such as maximizing accuracy. To address these goals, the thesis is organized into three complementary parts that examine and study human-AI interactions from the perspectives of both evaluated individuals and AI systems. Together, the work presented in this thesis advances human-centered machine learning by providing principles and methods for designing AI systems that align with human needs, values, and capabilities. Methodologically, this thesis integrates theoretical analysis, data-driven modeling, human-subject experiments, and empirical evaluations on real-world and semi-synthetic datasets.
