Strategic Classification

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Shaping Human-AI Interactions to Provide Improvement Pathways and Balance Competing Objectives
Aug 6, 2026Keziah NaggitaStrategic Classification Human-AI Interaction
TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO Chicago, Illinois
Robust Strategic Classification under Decision-Dependent Cost Uncertainty
Jun 29, 2026Sura Alhanouti, Güzin Bayraksan, Parinaz NaghizadehStrategic Classification Robust Optimization
Department of Integrated Systems Engineering, The Ohio State University, Ohio, USA · Department of Industrial Engineering, Jordan University of Science and Technology, Irbid, Jordan · Department of Electrical and Computer Engineering, University of California, San Diego, USA
Non-Linear Strategic Classification Made Practical
Jun 26, 2026Jack Geary, Boyan Gao, Henry GoukStrategic Classification Classification
School of Informatics, University of Edinburgh, Edinburgh, United Kingdom · Department of Engineering Science, University of Oxford, Oxford, United Kingdom
Strategic Feature Selection
Jun 17, 2026Jivat Neet Kaur, Pratik Patil, Divya Shanmugam +6Strategic Classification Feature Selection
University of California, Berkeley · University of Texas, Austin · Cornell Tech +4
Ambiguous Strategic Classification
Jun 8, 2026Ivri Hikri, Nir RosenfeldStrategic Classification
Faculty of Computer Science, Technion – Israel Institute of Technology, Haifa, Israel.
Off-Policy Evaluation with Strategic Agents via Local Disclosure
Jun 5, 2026Kiet Q. H. Vo, Abbavaram Gowtham Reddy, Julian Rodemann +2Strategic Classification Off-Policy Evaluation
Rational Intelligence Lab, CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Relational Machine Learning Lab, CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Department of Statistics, LMU Munich, Germany +1
Linear Strategic Classification with Endogenous Improvements
May 31, 2026Siddharth Shrivastava, Mahvith Akshintala, B Vamsha Vardhan Reddy +3Strategic Classification Classification
Department of Artificial Intelligence IIT Hyderabad · IIIT Hyderabad · DepartementIITofHyderabadArtifical Intelligence
Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
May 30, 2026Xinpeng Lv, Chunyuan Zheng, Yunxin Mao +9Strategic Classification Algorithmic Fairness
College of Computer Science and Technology, National University of Defense Technology · Institute for Theoretical Computer Science, Shanghai University of Finance and Economics · School of Mathematical Sciences, Peking University +3
Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
May 30, 2026Xinpeng Lv, Chunyuan Zheng, Yunxin Mao +8Strategic Classification Mechanism Design
National University of Defense Technology, Changsha, China · Peking University, Beijing, China · Shanghai University of Finance and Economics, Shanghai, China +2
The Role of Causal Features in Strategic Classification for Robustness and Alignment
May 26, 2026Antonio Gois, Sophia Gunluk, Nir Rosenfeld +3Strategic Classification OOD Generalization
Mila & Universit´e de Montr´eal · Faculty of Computer Science, Technion - Israel Institute of Technology · Canada CIFAR AI Chair +1
Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
May 19, 2026Xinpeng Lv, Yunxin Mao, Renzhe Xu +11Strategic Classification Bounded Rationality
College of Computer, National University of Defense Technology, Changsha, China · Institute for Theoretical Computer Science, Shanghai University of Finance and Economics, Shanghai, China · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China +3
When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
May 19, 2026Xinpeng Lv, Yunxin Mao, Renzhe Xu +13Tabular Foundation Models Strategic Classification
College of Computer, National University of Defense Technology, Changsha, China · Institute for Theoretical Computer Science, Shanghai University of Finance and Economics, Shanghai, China · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China +3
Strategic PAC Learnability via Geometric Definability
May 13, 2026Yuval Filmus, Shay Moran, Elizaveta Nesterova +2Strategic Classification PAC Learning
Faculty of Mathematics, Technion – Israel Institute of Technology. · Faculty of Computer Science, Technion – Israel Institute of Technology. · Faculty of Data and Decision Sciences, Technion – Israel Institute of Technology. +1
Sequential Strategic Classification with Multi-Stage Selective Classifiers
May 5, 2026Ziyuan Huang, Lina Alkarmi, Mingyan LiuStrategic Classification Sequential Decision Making
Department of Electrical and Computer Engineering University of Michigan Ann Arbor, MI 48105

Aug 6, 2026, cs.AIJ/K move · Enter open · S save

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

Keziah Naggita

TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO Chicago, Illinois

Strategic Classification Human-AI Interaction Human-AI Decision Making Algorithmic Recourse

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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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