Trust and Reliance in XAI -- Distinguishing Between Attitudinal and
Behavioral Measures
- URL: http://arxiv.org/abs/2203.12318v1
- Date: Wed, 23 Mar 2022 10:39:39 GMT
- Title: Trust and Reliance in XAI -- Distinguishing Between Attitudinal and
Behavioral Measures
- Authors: Nicolas Scharowski, Sebastian A. C. Perrig, Nick von Felten, Florian
Br\"uhlmann
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Trust is often cited as an essential criterion for the effective use and
real-world deployment of AI. Researchers argue that AI should be more
transparent to increase trust, making transparency one of the main goals of
XAI. Nevertheless, empirical research on this topic is inconclusive regarding
the effect of transparency on trust. An explanation for this ambiguity could be
that trust is operationalized differently within XAI. In this position paper,
we advocate for a clear distinction between behavioral (objective) measures of
reliance and attitudinal (subjective) measures of trust. However, researchers
sometimes appear to use behavioral measures when intending to capture trust,
although attitudinal measures would be more appropriate. Based on past
research, we emphasize that there are sound theoretical reasons to keep trust
and reliance separate. Properly distinguishing these two concepts provides a
more comprehensive understanding of how transparency affects trust and
reliance, benefiting future XAI research.
Related papers
- When to Trust LLMs: Aligning Confidence with Response Quality [49.371218210305656]
We propose CONfidence-Quality-ORDer-preserving alignment approach (CONQORD)
It integrates quality reward and order-preserving alignment reward functions.
Experiments demonstrate that CONQORD significantly improves the alignment performance between confidence and response accuracy.
arXiv Detail & Related papers (2024-04-26T09:42:46Z) - Trust and Transparency in Recommender Systems [0.0]
We first go through different understandings and measurements of trust in the AI and RS community, such as demonstrated and perceived trust.
We then review the relationsships between trust and transparency, as well as mental models, and investigate different strategies to achieve transparency in RS.
arXiv Detail & Related papers (2023-04-17T09:09:48Z) - Distrust in (X)AI -- Measurement Artifact or Distinct Construct? [0.0]
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.
arXiv Detail & Related papers (2023-03-29T07:14:54Z) - The Many Facets of Trust in AI: Formalizing the Relation Between Trust
and Fairness, Accountability, and Transparency [4.003809001962519]
Efforts to promote fairness, accountability, and transparency are assumed to be critical in fostering Trust in AI (TAI)
The lack of exposition on trust itself suggests that trust is commonly understood, uncomplicated, or even uninteresting.
Our analysis of TAI publications reveals numerous orientations which differ in terms of who is doing the trusting (agent), in what (object), on the basis of what (basis), in order to what (objective), and why (impact)
arXiv Detail & Related papers (2022-08-01T08:26:57Z) - 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 in AI and Its Role in the Acceptance of AI Technologies [12.175031903660972]
This paper explains the role of trust on the intention to use AI technologies.
Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students.
Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined.
arXiv Detail & Related papers (2022-03-23T19:18:19Z) - Uncertainty as a Form of Transparency: Measuring, Communicating, and
Using Uncertainty [66.17147341354577]
We argue for considering a complementary form of transparency by estimating and communicating the uncertainty associated with model predictions.
We describe how uncertainty can be used to mitigate model unfairness, augment decision-making, and build trustworthy systems.
This work constitutes an interdisciplinary review drawn from literature spanning machine learning, visualization/HCI, design, decision-making, and fairness.
arXiv Detail & Related papers (2020-11-15T17:26:14Z) - 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.