Abstract
Artificial Intelligence (AI) covers a broad spectrum of computational
problems and use cases. Many of those implicate profound and sometimes
intricate questions of how humans interact or should interact with AIs.
Moreover, many users or future users do have abstract ideas of what AI is,
significantly depending on the specific embodiment of AI applications.
Human-centered-design approaches would suggest evaluating the impact of
different embodiments on human perception of and interaction with AI. An
approach that is difficult to realize due to the sheer complexity of
application fields and embodiments in reality. However, here XR opens new
possibilities to research human-AI interactions. The article's contribution is
twofold: First, it provides a theoretical treatment and model of human-AI
interaction based on an XR-AI continuum as a framework for and a perspective of
different approaches of XR-AI combinations. It motivates XR-AI combinations as
a method to learn about the effects of prospective human-AI interfaces and
shows why the combination of XR and AI fruitfully contributes to a valid and
systematic investigation of human-AI interactions and interfaces. Second, the
article provides two exemplary experiments investigating the aforementioned
approach for two distinct AI-systems. The first experiment reveals an
interesting gender effect in human-robot interaction, while the second
experiment reveals an Eliza effect of a recommender system. Here the article
introduces two paradigmatic implementations of the proposed XR testbed for
human-AI interactions and interfaces and shows how a valid and systematic
investigation can be conducted. In sum, the article opens new perspectives on
how XR benefits human-centered AI design and development.
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