设计可引导用户正确认知并优化行为的AI交互机制
Shaping Human-AI Interactions to Provide Improvement Pathways and Balance Competing Objectives
- 构建人机交互循环，通过反馈引导用户形成准确认知
- 实验证明该机制能有效减少投机行为，提升整体系统准确性
- 适合关注AI可解释性与用户行为激励的研究者和从业者
当AI系统投入应用时，使用者或被评估者会基于对系统运作方式的认知，策略性地呈现自身偏好、行为或属性。系统随之提供反馈或决策结果，形成人机交互闭环。本文研究如何设计和塑造此类交互，以达成三个目标：(1) 帮助个体建立对AI系统的准确认知，从而以最低成本实现自我改进或获得有利结果；(2) 鼓励真实改善行为，抑制投机取巧；(3) 确保系统持续实现其预定目标，如最大化准确率。论文从被评估者和系统双重视角，分三部分开展理论分析、数据建模、真人实验及真实与半合成数据集上的实证评估，提出一系列人本机器学习原则与方法。
原文摘要 · Abstract (English)
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.
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