Twin agents as personal digital representations create distinct trust calibration challenges because they dissolve the boundary between AI and human decision-makers, unlike existing frameworks designed for clear separation.
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing , author=
10 Pith papers cite this work, alongside 437 external citations. Polarity classification is still indexing.
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SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
Proposes the theory of epistemic quasi-partnerships (EQP) to guide the RCC approach (reasons, counterfactuals, confidence) for human-grounded explanations in AI decision support systems.
A cross-lingual QA framework shows users build stronger mental models of MT systems through practice and source language knowledge mainly by spotting surface-level errors, with transcriptions helping further.
Semi-structured interviews with 13 early adopters in one large tech organization identify multiple framings of transparency and synthesize them into a developer-user-governance multidimensional framework.
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
Structural mental models of AI writing assistants improve system understanding and usability but result in more grammatical errors in user writing compared to functional models.
Industry markets AI agents for orchestration, creation, and insight, but a usability study with 31 participants reveals users face challenges from capability misalignment and lack of meta-cognition in tools like Operator and Manus.
Qualitative studies show creatives prefer self-experimentation over structured guidance for GenAI image tools to preserve creative autonomy despite terminology barriers.
citing papers explorer
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From Role to Person: Trust Calibration Challenges in Twin Agents
Twin agents as personal digital representations create distinct trust calibration challenges because they dissolve the boundary between AI and human decision-makers, unlike existing frameworks designed for clear separation.
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Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science
SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
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Explainable and Human-Grounded AI for Decision Support Systems: The Theory of Epistemic Quasi-Partnerships
Proposes the theory of epistemic quasi-partnerships (EQP) to guide the RCC approach (reasons, counterfactuals, confidence) for human-grounded explanations in AI decision support systems.
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Measuring User's Mental Models of Speech Translation in Human-AI Collaboration
A cross-lingual QA framework shows users build stronger mental models of MT systems through practice and source language knowledge mainly by spotting surface-level errors, with transcriptions helping further.
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"So There's a Catch-22 Here": How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparency
Semi-structured interviews with 13 early adopters in one large tech organization identify multiple framings of transparency and synthesize them into a developer-user-governance multidimensional framework.
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Effects of Generative AI Errors on User Reliance Across Task Difficulty
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
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Addressing the Synergy Gap: The Six Elements of the Design Space
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
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From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants
Structural mental models of AI writing assistants improve system understanding and usability but result in more grammatical errors in user writing compared to functional models.
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Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agents
Industry markets AI agents for orchestration, creation, and insight, but a usability study with 31 participants reveals users face challenges from capability misalignment and lack of meta-cognition in tools like Operator and Manus.
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How Creatives Approach GenAI Image Generation: Tensions Between Structured Guidance, Self-Experimentation, and Creative Autonomy
Qualitative studies show creatives prefer self-experimentation over structured guidance for GenAI image tools to preserve creative autonomy despite terminology barriers.