Distrust in (X)AI -- Measurement Artifact or Distinct Construct?
- URL: http://arxiv.org/abs/2303.16495v1
- Date: Wed, 29 Mar 2023 07:14:54 GMT
- Title: Distrust in (X)AI -- Measurement Artifact or Distinct Construct?
- Authors: Nicolas Scharowski, Sebastian A. C. Perrig
- Abstract summary: Trust is a key motivation in developing explainable artificial intelligence (XAI)
Distrust seems relatively understudied in XAI.
psychometric evidence favors a distinction between trust and distrust.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Trusting Your AI Agent Emotionally and Cognitively: Development and Validation of a Semantic Differential Scale for AI Trust [16.140485357046707]
We develop and validated a set of 27-item semantic differential scales for affective and cognitive trust.
Our empirical findings showed how the emotional and cognitive aspects of trust interact with each other and collectively shape a person's overall trust in AI agents.
arXiv Detail & Related papers (2024-07-25T18:55:33Z) - A Diachronic Perspective on User Trust in AI under Uncertainty [52.44939679369428]
Modern NLP systems are often uncalibrated, resulting in confidently incorrect predictions that undermine user trust.
We study the evolution of user trust in response to trust-eroding events using a betting game.
arXiv Detail & Related papers (2023-10-20T14:41:46Z) - KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph
Neural Networks [63.531790269009704]
Social Internet of Things (SIoT) is a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things)
Due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation.
We propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT.
arXiv Detail & Related papers (2023-02-22T14:24:45Z) - Designing for Responsible Trust in AI Systems: A Communication
Perspective [56.80107647520364]
We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH.
We highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users.
We propose a checklist of requirements to help technology creators identify appropriate cues to use.
arXiv Detail & Related papers (2022-04-29T00:14:33Z) - Trust and Reliance in XAI -- Distinguishing Between Attitudinal and
Behavioral Measures [0.0]
Researchers argue that AI should be more transparent to increase trust, making transparency one of the main goals of XAI.
empirical research on this topic is inconclusive regarding the effect of transparency on trust.
We advocate for a clear distinction between behavioral (objective) measures of reliance and attitudinal (subjective) measures of trust.
arXiv Detail & Related papers (2022-03-23T10:39:39Z) - Trust in AI: Interpretability is not necessary or sufficient, while
black-box interaction is necessary and sufficient [0.0]
The problem of human trust in artificial intelligence is one of the most fundamental problems in applied machine learning.
We draw from statistical learning theory and sociological lenses on human-automation trust to motivate an AI-as-tool framework.
We clarify the role of interpretability in trust with a ladder of model access.
arXiv Detail & Related papers (2022-02-10T19:59:23Z) - Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and
Goals of Human Trust in AI [55.4046755826066]
We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i.e., trust between people)
We incorporate a formalization of 'contractual trust', such that trust between a user and an AI is trust that some implicit or explicit contract will hold.
We discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted.
arXiv Detail & Related papers (2020-10-15T03:07:23Z) - Where Does Trust Break Down? A Quantitative Trust Analysis of Deep
Neural Networks via Trust Matrix and Conditional Trust Densities [94.65749466106664]
We introduce the concept of trust matrix, a novel trust quantification strategy.
A trust matrix defines the expected question-answer trust for a given actor-oracle answer scenario.
We further extend the concept of trust densities with the notion of conditional trust densities.
arXiv Detail & Related papers (2020-09-30T14:33:43Z) - How Much Can We Really Trust You? Towards Simple, Interpretable Trust
Quantification Metrics for Deep Neural Networks [94.65749466106664]
We conduct a thought experiment and explore two key questions about trust in relation to confidence.
We introduce a suite of metrics for assessing the overall trustworthiness of deep neural networks based on their behaviour when answering a set of questions.
The proposed metrics are by no means perfect, but the hope is to push the conversation towards better metrics.
arXiv Detail & Related papers (2020-09-12T17:37:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.