Shaping Human-AI Interactions to Provide Improvement Pathways and Balance Competing Objectives

2026-08-06

AI Summary

This work addresses the challenge that users often adopt gaming strategies against deployed AI systems due to misconceptions about their mechanisms, undermining both personal development and system objectives such as predictive accuracy. To counter this, the study proposes a novel human-AI interaction design principle that systematically integrates the perspectives of both the evaluated individuals and the AI system itself. This approach simultaneously incentivizes genuine self-improvement and preserves system performance. Through theoretical analysis, data-driven modeling, human-subject experiments, and validation on real and semi-synthetic datasets, the research demonstrates that the proposed method effectively calibrates user beliefs, substantially reduces gaming behaviors, fosters meaningful self-enhancement, and maintains the stability of core AI performance metrics. The findings offer a new paradigm for the trustworthy deployment of AI in practice.

Abstract

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.

Problem

• Human-AI interaction
• strategic behavior
• belief formation
• gaming behavior
• system objectives

Innovation

• human-AI interaction
• behavioral shaping
• gaming mitigation
• human-centered machine learning
• feedback loop design