[CogSci] Call for papers: Workshop on Trust and Reliance in AI-Human Teams (TRAIT)

[Ken Holstein]

Announcing the first workshop on *Trust and Reliance in AI-Human Teams
(TRAIT) *at CHI 2022 <https://chi-trait.github.io/#/cfp>!

As humans increasingly interact with AI systems during decision-making,
creative tasks, and other workflows, appropriate trust and reliance are
necessary to ensure proper usage and adoption of these systems. For
example, people should understand when to trust or rely on an algorithm’s
outputs and when to override them. While significant research focus has
aimed to measure and promote trust in human-AI interaction, the field lacks
synthesized results across contexts, formalized key concepts, and
definitions.

The workshop will provide a venue to explore three broad aspects related to
human-AI trust:

   1. How do we *clarify definitions and frameworks* relevant to human-AI
   trust and reliance (e.g., what does trust mean in different contexts)?
   2. How do we *measure* trust and reliance?
   3. How do we *shape* trust and reliance?

Major themes include, but are not limited to:

   - Definitions of trust and reliance
   - Human-human trust and lessons from social sciences
   - Qualitative (e.g., user reflection) and quantitative methods (e.g.,
   usage, adoption, team performance, etc.) for evaluating trust and reliance.
   - Tradeoffs with other objectives (e.g., team performance, creativity,
   etc)
   - Solutions (and their limitations) for promoting appropriate trust
   (e.g., XAI, control mechanisms, human agency, communicating uncertainty
   etc).
   - Safety mechanisms for situations in which trust is broken.


*Call for papers:*
We are accepting brief (2-6 page) papers, which may include position
papers, in-progress works, practitioner perspectives, or syntheses of
existing research. See more at: https://chi-trait.github.io/#/cfp


*Submission deadline:*February 11th (AoE)

--
Ken Holstein
Assistant Professor of Human–Computer Interaction
HCI Institute, School of Computer Science, Carnegie Mellon University*.*
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