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Human-AI interaction is evolving rapidly, revealing critical insights into how AI systems fail and succeed in real-world applications. Recent studies show that a significant portion of AI failures are invisible, often unnoticed by users, which complicates the development of reliable AI systems. Additionally, the dynamics of human-AI cooperation highlight the importance of transparency and adaptability in AI design, as these factors significantly influence user experience and outcomes. Research also indicates that while AI can enhance decision-making and learning, it may inadvertently reduce users' persistence and independent performance. As AI becomes more integrated into daily life, understanding these interactions is essential for builders aiming to create effective, user-centered AI solutions that foster genuine collaboration and trust between humans and machines.
Topic-specific paper and score movement from the daily diff ledger.
AI systems fail silently far more often than they fail visibly. In a large-scale quantitative analysis of human-AI interactions from the WildChat dataset, we find that 78% of AI failures are invisible...
We study the ongoing debate regarding the statistical fidelity of AI-generated data compared to human-generated data in the context of non-verbal communication using full body motion. Concretely, we a...
AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and joint impacts are underexplored in imperfectly coopera...
AI-based writing assistants are ubiquitous, yet little is known about how users' mental models shape their use. We examine two types of mental models -- functional or related to what the system does, ...
Just as people improve decision-making by consulting diverse human advisors, they can now also consult with multiple AI systems. Prior work on group decision-making shows that advice aggregation creat...
As AI-generated and AI-assisted content floods online spaces, source labels attached to such content can distort human reasoning judgments, with downstream consequences for moderation, evaluation, and...
Generative AI (genAI) is increasingly being integrated into children's everyday lives, not only through screens but also through so-called "screen-free" AI toys. These toys can simulate emotions, pers...
Although AI assistants are now deeply embedded in society, there has been limited empirical study of how their usage affects human empowerment. We present the first large-scale empirical analysis of d...
As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction are needed. We introduce two theoretically grounde...
Millions of people now turn to artificial intelligence (AI) systems for personal advice, guidance, and support. Such systems can be sycophantic, frequently affirming users' views and beliefs. Across f...
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Canonical route: /topics
Agent Handoff
Canonical ID human-ai-interaction | Route /topic/human-ai-interaction
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/human-ai-interactionMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Human-AI Interaction",
"cluster": "Human-AI Interaction"
}
}source_context
{
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"mode": "topic",
"query": "Human-AI Interaction",
"normalized_query": "human-ai-interaction",
"route": "/topic/human-ai-interaction",
"paper_ref": null,
"topic_slug": "human-ai-interaction",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.