  Ensuring quality human-AI interaction (HAII) in safety-critical industries is
essential. Failure to do so can lead to catastrophic and deadly consequences.
Despite this urgency, existing research on HAII is limited, fragmented, and
inconsistent. We present here a survey of that literature and recommendations
for research best practices that should improve the field. We divided our
investigation into the following areas: 1) terms used to describe HAII, 2)
primary roles of AI-enabled systems, 3) factors that influence HAII, and 4) how
HAII is measured. Additionally, we described the capabilities and maturity of
the AI-enabled systems used in safety-critical industries discussed in these
articles. We found that no single term is used across the literature to
describe HAII and some terms have multiple meanings. According to our
literature, seven factors influence HAII: user characteristics (e.g., user
personality), user perceptions and attitudes (e.g., user biases), user
expectations and experience (e.g., mismatched user expectations and
experience), AI interface and features (e.g., interactive design), AI output
(e.g., perceived accuracy), explainability and interpretability (e.g., level of
detail, user understanding), and usage of AI (e.g., heterogeneity of
environments). HAII is most measured with user-related subjective metrics
(e.g., user perceptions, trust, and attitudes), and AI-assisted decision-making
is the most common primary role of AI-enabled systems. Based on this review, we
conclude that there are substantial research gaps in HAII. Researchers and
developers need to codify HAII terminology, involve users throughout the AI
lifecycle (especially during development), and tailor HAII in safety-critical
industries to the users and environments.
