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

arXiv: https://arxiv.org/abs/2608.05710 · HTML: https://mlsanity.com/paper/2608.05710/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 193 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems](https://mlsanity.com/paper/1907.09615/) (2019)
- [Equalizing Recourse across Groups](https://mlsanity.com/paper/1909.03166/) (2019)
- [Gaming Helps! Learning from Strategic Interactions in Natural Dynamics](https://mlsanity.com/paper/2002.07024/) (2020)
- [Extracting Incentives from Black-Box Decisions](https://mlsanity.com/paper/1910.05664/) (2019)
- [Comparison of 14 different families of classification algorithms on 115 binary datasets](https://mlsanity.com/paper/1606.00930/) (2016)
- [Regularized Robustly Reliable Learners and Instance Targeted Attacks](https://mlsanity.com/paper/2410.10572/) (2024)
- [How Strategic Agents Respond: Comparing Analytical Models with LLM-Generated Responses in Strategic Classification](https://mlsanity.com/paper/2501.16355/) (2025)
- [Private Information, Credit Risk and Graph Structure in P2P Lending Networks](https://mlsanity.com/paper/1802.10000/) (2018)
- [Min-Max Kernels](https://mlsanity.com/paper/1503.01737/) (2015)
- [The Active and Noise-Tolerant Strategic Perceptron](https://mlsanity.com/paper/2512.01783/) (2025)
- [Revealing Positive and Negative Role Models to Help People Make Good Decisions](https://mlsanity.com/paper/2603.02495/) (2026)

## Abstract (arXiv)

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.