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NSF AI Research Institute on Interaction for AI Assistants
Brown University.
NSF AI Research Institute on Interaction for AI Assistants
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  • Areas of Study

    • Trustworthy Multimodal Interaction

      Studying how people perceive and interact with AI across speech, vision, text, and gesture to build trustworthy systems.

    • Reasoning in Humans and AI

      Exploring how humans and AI systems reason differently and what this means for collaboration and decision-making.

    • Alignment in Subjective Domains

      Investigating how AI can align with diverse human values, preferences, and cultural contexts in areas without clear-cut answers.

    • Participatory Design

      Centering the people most affected by AI in the design process, especially communities traditionally excluded from tech development.

    ARIA Insights

    Our Latest Insights
    Gesture as a First-Class Input Modality
    Cross-Cultural Value Alignment in LLMs
    Consent and Data Flows in Youth-Facing Assistants
    Visualizing Model Reasoning for Non-Experts
    Listening Tour: Mental Health Clinicians on AI
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    • Get Involved with ARIA Today!
    • View our YouTube Channel
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    • Get Involved with ARIA Today!
    • View our YouTube Channel
    • View our Google Scholar Page
  • News & Events
  • Get Involved with ARIA Today!
  • View our YouTube Channel
  • View our Google Scholar Page
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Trustworthy Multimodal Interaction

Related Insights, News & Events for Trustworthy Multimodal Interaction

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  • Article

    Multimodal Benchmarks for Assistant Reliability

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam nec nibh vel ante fermentum ullamcorper. Sed dapibus euismod nisl sed accumsan.

    April 13, 2026
    Read More
  • Article

    Gesture as a First-Class Input Modality

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam nec nibh vel ante fermentum ullamcorper. Sed dapibus euismod nisl sed accumsan.

    April 13, 2026
    Read More
  • Article

    Trust Calibration in Voice-Based AI Assistants

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed dapibus euismod nisl sed accumsan.

    February 10, 2026
    Read More

Areas of Study

  • Trustworthy Multimodal Interaction

    Studying how people perceive and interact with AI across speech, vision, text, and gesture to build trustworthy systems.

    Learn More
  • Reasoning in Humans and AI

    Exploring how humans and AI systems reason differently and what this means for collaboration and decision-making.

    Learn More
  • Alignment in Subjective Domains

    Investigating how AI can align with diverse human values, preferences, and cultural contexts in areas without clear-cut answers.

    Learn More
  • Participatory Design

    Centering the people most affected by AI in the design process, especially communities traditionally excluded from tech development.

    Learn More

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Contact Us
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Trustworthy Multimodal Interaction
Reasoning in Humans and AI
Alignment in Subjective Domains
Participatory Design
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For Researchers
For Students
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Glossary
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Brown University.
© 2026 Brown University. All rights reserved.
Supported by the National Science Foundation under Award No. 2433429
National Science Foundation and NSF AI Institutes Virtual Organization.

Glossary

A quick guide to key terms shaping ARIA's work at the intersection of AI and human experience.

  • Alignment

    The challenge of ensuring that AI systems behave in ways consistent with human values, intentions, and expectations—particularly in domains where those values are subjective or contested.

  • Artificial Intelligence (AI)

    The development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language understanding.

  • Cognitive Science

    The interdisciplinary study of the mind and its processes, drawing on psychology, neuroscience, linguistics, philosophy, and computer science.

  • Explainability

    The degree to which the internal workings or outputs of an AI system can be understood by a human. Also called interpretability or transparency.

  • Human-Robot Interaction (HRI)

    The study of how humans and robots communicate, collaborate, and coexist, with attention to social, cognitive, and physical dimensions of their interactions.

  • Machine Learning

    A subset of AI in which systems learn from data and improve their performance on specific tasks without being explicitly programmed for each case.

  • Multimodal Interaction

    Communication between humans and AI systems that involves multiple channels or modes, such as speech, text, gesture, vision, and touch.

  • Natural Language Processing

    A field of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.

  • Participatory Design

    A design approach that actively involves the people who will be affected by a system in the process of designing it, ensuring their needs, values, and perspectives shape the outcome.

  • Trust Calibration

    The process by which a person adjusts their level of trust in an AI system based on its performance, transparency, and reliability over time.

People Shaping ARIA Today

Brown University

  • Ellie Pavlick (Director)
  • Alexander Batten (Managing Director)
  • Bertram Malle
  • Chen Sun
  • Diana Freed
  • Drew Linsley
  • George Konidaris
  • Kathi Fisler
  • Michael Frank
  • Roman Feiman
  • Shriram Krishnamurthi
  • Stefanie Tellex
  • Stephen Bach
  • Steve Sloman
  • Suresh Venkatasubramanian
  • Thomas Serre

Carnegie Mellon University

  • Henny Admoni
  • Reid Simmons

Colby College

  • David Watts
  • Ike Lage
  • Veronica Romero

Dartmouth College

  • Andrew Campbell
  • Lisa Marsch
  • Nicholas Jacobson
  • Steven Frankland

Data & Society

  • Alice Marwick
  • Briana Vecchione
  • Livia Garofalo
  • Patrick Davison

New York University

  • Grace Lindsay
  • Tal Linzen

Santa Fe Institute

  • Melanie Mitchell

UC Berkeley

  • Alison Gopnik
  • Bruno Olshausen
  • Celeste Kidd
  • Pentti Kanerva

UC San Diego

  • Berk Ustun
  • David Danks

University of New Mexico

  • Melanie Moses
  • Sonia Rankin