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Assessment and Communication for People with Disorders of Consciousness

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Published on: August 1, 2017

How Terminology Affects Users' Responses to System Failures.

Cindy Candrian¹, Anne Scherer²

¹Delta Labs AG, Zurich, Switzerland.

|September 21, 2023

Summary

This summary is machine-generated.

Using "AI" instead of "algorithmic" for systems improves user behavior after errors. Strategic terminology can mitigate algorithm aversion and enhance trust in AI and algorithmic systems.

Keywords:

expert systems human-automation interaction system design technology acceptance trust in automation

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Area of Science:

Human-Computer Interaction
Behavioral Economics
Artificial Intelligence Ethics

Background:

Ambiguity in research regarding trust in systems due to interchangeable terminology.
Lack of clear understanding of how system descriptions influence user perception and behavior.

Purpose of the Study:

To investigate the impact of system terminology on consumer behavior following system errors.
To explore how dynamic trust and attribution theory explain user responses to system failures.
To provide guidance on mitigating algorithm aversion through strategic terminology.

Main Methods:

Three empirical studies examining the effect of system terminology on consumer behavior.
Analysis of behavioral responses to system failures under different naming conventions (e.g., "AI" vs. "algorithmic").

Main Results:

System terminology significantly influences user behavior and trust.
Describing a system as "AI" (self-learning, complex) elicits more favorable user responses to errors than describing it as "algorithmic" (rule-based, less complex).

Conclusions:

Terminology is crucial and can unintentionally affect research robustness and replicability.
When "AI" is not applicable, explaining errors and task complexity can improve user acceptance.
Strategic use of terminology can enhance user trust and acceptance of AI and algorithmic systems.