arXiv AI
Jun 12
Strategic Decision Support for AI Agents
arXiv:2606. 12587v1 Announce Type: new Abstract: Traditionally, decision support studies how humans use machine learning models to make better decisions.
A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice
arXiv:2606. 06081v1 Announce Type: new Abstract: Appropriate reliance on AI advice has become a central research theme in human-AI collaboration.
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arXiv Machine Learning
Sep 16
Strategic Advice in the Age of Personal AI
The paper examines how advisors should tailor recommendations when users consult personal AI assistants whose advice is predictable. It models the influence of personal AI through consultation probability and relative trust, finding that optimal counteraction and loss are hump‑shaped in these dimensions. The study also explores partial predictability, costly adjustments, richer information structures, and presents an online experiment showing participants weigh advisor, personal AI, and their own judgments differently.
arXiv AI
Jul 7
Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making
arXiv:2607. 03025v1 Announce Type: new Abstract: The use of Large Language Models (LLMs) across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhance decision-making effectiveness with minimal human feedback.
arXiv AI
Aug 14
Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning
arXiv:2608. 12372v1 Announce Type: new Abstract: AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers.
arXiv Computation and Language
Sep 1
Thesis Proposal: Toward a Human-Centered and Perspective-Aware Framework for Reproducible ML Evaluation and AI Alignment
arXiv:2608.30842v1 Announce Type: new Abstract: Humans play a vital role at every stage of AI development, from data collection and curation to model development and evaluation. However, humans often...
arXiv AI
Aug 13
On Benchmarking Human-Like Intelligence in Machines
arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.
arXiv Machine Learning
Jul 9
Robust Human-AI Complementarity under Uncertainty
arXiv:2607. 06656v1 Announce Type: new Abstract: Machine learning models are often intended to augment rather than replace human decision makers, by providing information that is complementary to human judgement.
arXiv AI
Aug 7
Shaping Human-AI Interactions to Provide Improvement Pathways and Balance Competing Objectives
arXiv:2608. 05710v1 Announce Type: new 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.
arXiv AI
Aug 19
Supporting Calibrated Reliance in Human-AI Collaboration: Different Strategies for Different Tasks
The study investigates how different AI support formats influence human decision-making across two tasks: abstract visual reasoning with RAVEN matrices and deductive logical reasoning with LSAT problems. Findings reveal that in visual reasoning, predictions alone and predicted probabilities best support accuracy and error recovery, while in logical reasoning, LLM explanations outperform other supports. The results suggest that effective human–AI collaboration requires task‑specific support strategies rather than a one‑size‑fits‑all approach.
arXiv AI
Jun 26
Human-AI Complementarity: A Goal for Amplified Oversight
arXiv:2510. 26518v2 Announce Type: replace Abstract: Human feedback is critical for aligning AI systems to human values.
arXiv AI
Aug 12
Selective Prediction Reduces the Negative Effects of Automation Bias Overall but Increases False Negatives
arXiv:2508. 07617v2 Announce Type: replace-cross Abstract: AI has the potential to augment human decision making.
Hugging Face Trending Papers
Jul 15
AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized
Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one direct replication), participants answered difficult questions and could always decline to respond.