Metrics for explainable AI: Challenges and prospects
The question addressed in this paper is: If we present to a user an AI system that explains
how it works, how do we know whether the explanation works and the user has achieved a …
how it works, how do we know whether the explanation works and the user has achieved a …
[HTML][HTML] Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance
If a user is presented an AI system that portends to explain how it works, how do we know
whether the explanation works and the user has achieved a pragmatic understanding of the …
whether the explanation works and the user has achieved a pragmatic understanding of the …
Supporting Human-AI Teams: Transparency, explainability, and situation awareness
MR Endsley - Computers in Human Behavior, 2023 - Elsevier
Abstract System autonomy and AI are being developed for a wide variety of applications
where they will likely work in tandem with people, forming human-AI teams (HAT). Situation …
where they will likely work in tandem with people, forming human-AI teams (HAT). Situation …
Kagnet: Knowledge-aware graph networks for commonsense reasoning
Commonsense reasoning aims to empower machines with the human ability to make
presumptions about ordinary situations in our daily life. In this paper, we propose a textual …
presumptions about ordinary situations in our daily life. In this paper, we propose a textual …
Graph networks as learnable physics engines for inference and control
A Sanchez-Gonzalez, N Heess… - International …, 2018 - proceedings.mlr.press
Understanding and interacting with everyday physical scenes requires rich knowledge
about the structure of the world, represented either implicitly in a value or policy function, or …
about the structure of the world, represented either implicitly in a value or policy function, or …
From word models to world models: Translating from natural language to the probabilistic language of thought
How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …
meaning from language--and how can we leverage a theory of linguistic meaning to build …
The role of shared mental models in human-AI teams: a theoretical review
RW Andrews, JM Lilly, D Srivastava… - Theoretical Issues in …, 2023 - Taylor & Francis
Mental models are knowledge structures employed by humans to describe, explain, and
predict the world around them. Shared Mental Models (SMMs) occur in teams whose …
predict the world around them. Shared Mental Models (SMMs) occur in teams whose …
Affordances of augmented reality in science learning: Suggestions for future research
KH Cheng, CC Tsai - Journal of science education and technology, 2013 - Springer
Augmented reality (AR) is currently considered as having potential for pedagogical
applications. However, in science education, research regarding AR-aided learning is in its …
applications. However, in science education, research regarding AR-aided learning is in its …
Drawing-to-learn: a framework for using drawings to promote model-based reasoning in biology
The drawing of visual representations is important for learners and scientists alike, such as
the drawing of models to enable visual model-based reasoning. Yet few biology instructors …
the drawing of models to enable visual model-based reasoning. Yet few biology instructors …
Cultural conceptualisations and language
F Sharifian - 2011 - torrossa.com
This book covers the theoretical framework of cultural conceptualisations, cultural cognition
and language which I have been developing since 2001. It draws on a multidisciplinary …
and language which I have been developing since 2001. It draws on a multidisciplinary …