Evaluation of a Scale to Assess Subjective Information Processing Awareness of Humans in Interaction With Automation & Artificial Intelligence
Allgemeines
Art der Publikation: Conference Paper
Veröffentlicht auf / in: Artificial Intelligence and Social Computing : Proceedings of the 15th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, Nice, France, 24-27 July 2024
Jahr: 2024
Band / Volume: 122
Seiten: 41-51
Verlag (Publisher): AHFE Open Access
DOI: https://doi.org/10.54941/ahfe1004635
ISBN: 978-1-958651-98-8
Autoren
Zusammenfassung
Subjective Information Processing Awareness (SIPA) describes how users experience
the extent to which a system enables them to perceive, understand and predict its
information processing. With the rising interdependence of information processing in
Human-AI interaction, research methods for assessing user experience in automated
information processing are needed. The objective of the present research was the
empirical evaluation of the SIPA scale as an economical method to assess SIPA, as well
as the construction of and comparison with a version in plain language. To this end,
two empirical studies were conducted (NS1 = 317, NS2 = 230) to enable scale analysis.
Results showed that the SIPA scale achieves excellent reliability and expected correla-
tions with connected constructs, e.g., trust and perceived usefulness of AI systems. In
addition, no benefits of a plain language variant were found. Based on the results, the
SIPA scale appears to be a promising tool for examining user experience of systems
with automated information processing.
the extent to which a system enables them to perceive, understand and predict its
information processing. With the rising interdependence of information processing in
Human-AI interaction, research methods for assessing user experience in automated
information processing are needed. The objective of the present research was the
empirical evaluation of the SIPA scale as an economical method to assess SIPA, as well
as the construction of and comparison with a version in plain language. To this end,
two empirical studies were conducted (NS1 = 317, NS2 = 230) to enable scale analysis.
Results showed that the SIPA scale achieves excellent reliability and expected correla-
tions with connected constructs, e.g., trust and perceived usefulness of AI systems. In
addition, no benefits of a plain language variant were found. Based on the results, the
SIPA scale appears to be a promising tool for examining user experience of systems
with automated information processing.