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Key Takeaways

Trust and distrust may not be opposites: The paper challenges the common assumption that distrust is simply the absence of trust. Instead, psychological research suggests they are two separate constructs that can exist independently—meaning someone could simultaneously have low trust AND low distrust in an AI system, or high levels of both.
Current XAI research may be missing half the picture: Most explainable AI studies focus on building trust through better explanations, but largely ignore distrust as a distinct phenomenon. This oversight could mean researchers are not fully understanding why people reject or remain skeptical of AI systems even when given explanations.
Measurement challenges complicate trust research: The paper identifies multiple obstacles in studying trust in AI, including inconsistent definitions of trust across studies, overly simplified experimental tasks that don't reflect real-world uncertainty, and the lack of validated questionnaires specifically designed for AI contexts—making it difficult to compare findings across different research projects.
The distinction between trust and distrust remains unresolved: While psychological theory supports treating trust and distrust as separate constructs, the paper notes it's still unclear whether current measurement methods actually capture this distinction or whether apparent differences are merely artifacts of how researchers design their studies.
The XAI community should investigate this distinction further: The authors call for more research to determine whether trust and distrust truly operate as independent constructs in AI contexts, which could lead to more effective strategies for building appropriate user confidence in AI systems.

Abstract

Trust is a key motivation in developing explainable artificial intelligence (XAI). However, researchers attempting to measure trust in AI face numerous challenges, such as different trust conceptualizations, simplified experimental tasks that may not induce uncertainty as a prerequisite for trust, and the lack of validated trust questionnaires in the context of AI. While acknowledging these issues, we have identified a further challenge that currently seems underappreciated - the potential distinction between trust as one construct and \emph{distrust} as a second construct independent of trust. While there has been long-standing academic discourse for this distinction and arguments for both the one-dimensional and two-dimensional conceptualization of trust, distrust seems relatively understudied in XAI. In this position paper, we not only highlight the theoretical arguments for distrust as a distinct construct from trust but also contextualize psychometric evidence that likewise favors a distinction between trust and distrust. It remains to be investigated whether the available psychometric evidence is sufficient for the existence of distrust or whether distrust is merely a measurement artifact. Nevertheless, the XAI community should remain receptive to considering trust and distrust for a more comprehensive understanding of these two relevant constructs in XAI.

Key Takeaways

Trust and distrust may not be opposites: The paper challenges the common assumption that distrust is simply the absence of trust. Instead, psychological research suggests they are two separate constructs that can exist independently—meaning someone could simultaneously have low trust AND low distrust in an AI system, or high levels of both.
Current XAI research may be missing half the picture: Most explainable AI studies focus on building trust through better explanations, but largely ignore distrust as a distinct phenomenon. This oversight could mean researchers are not fully understanding why people reject or remain skeptical of AI systems even when given explanations.
Measurement challenges complicate trust research: The paper identifies multiple obstacles in studying trust in AI, including inconsistent definitions of trust across studies, overly simplified experimental tasks that don't reflect real-world uncertainty, and the lack of validated questionnaires specifically designed for AI contexts—making it difficult to compare findings across different research projects.
The distinction between trust and distrust remains unresolved: While psychological theory supports treating trust and distrust as separate constructs, the paper notes it's still unclear whether current measurement methods actually capture this distinction or whether apparent differences are merely artifacts of how researchers design their studies.
The XAI community should investigate this distinction further: The authors call for more research to determine whether trust and distrust truly operate as independent constructs in AI contexts, which could lead to more effective strategies for building appropriate user confidence in AI systems.

Abstract

Distrust in (X)AI -- Measurement Artifact or Distinct Construct?

Key Takeaways

Abstract

Paper Info

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Distrust in (X)AI -- Measurement Artifact or Distinct Construct?

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links