Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stereotype Content Model02:16

Stereotype Content Model

15.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.0K
Equity Theory01:26

Equity Theory

50
Equity theory explains how our sense of fairness influences the dynamics of close relationships. Rooted in social psychology, the theory posits that individuals evaluate fairness by comparing the ratio of their contributions to the rewards they receive. Relationship satisfaction is highest when these ratios are perceived as balanced between partners, promoting mutual reciprocity and a sense of justice.Equity vs. Equality in RelationshipsEquity is distinct from equality. Fairness does not...
50
Strategies of Self-Presentation I: Strategic Self-Presentation01:12

Strategies of Self-Presentation I: Strategic Self-Presentation

50
Strategic self-presentation refers to individuals' intentional efforts to influence how others perceive them. This process is employed in various social and professional settings, such as job interviews, dating, politics, and legal contexts, where individuals seek to shape impressions to gain social or material advantages. While people generally present themselves in ways that align with their authentic characteristics, external factors, such as cognitive load, can hinder their ability to...
50
Instrument Calibration01:12

Instrument Calibration

360
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
360
Confirmation Biases01:31

Confirmation Biases

7.4K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
7.4K
Social Proof00:52

Social Proof

29.8K
Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.
29.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Serious Game Aimed at Assessing Executive Planning Skills in Children With Autism: Cross-Sectional Design and Formative Evaluation of ShopAutiPlan.

JMIR serious games·2026
Same author

ShopAutiPlan: Validating a Serious Game for Assessing Executive Planning in Autism.

Autism research : official journal of the International Society for Autism Research·2026
Same author

A Systematic Review on Computer Vision in Play-Based Research and Interventions for Autistic Children.

Autism research : official journal of the International Society for Autism Research·2026
Same author

Joint attention in autism: A narrative review of assessment techniques from behavioral observation to artificial intelligence.

Behavior research methods·2026
Same author

Automated dispensing cabinets and nurse-related medication errors in inpatient settings: A systematic review.

Exploratory research in clinical and social pharmacy·2026
Same author

Hype vs Reality in the Integration of Artificial Intelligence in Clinical Workflows.

JMIR formative research·2025

Related Experiment Video

Updated: Oct 25, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

783

Explainable recommendation: when design meets trust calibration.

Mohammad Naiseh1, Dena Al-Thani2, Nan Jiang1

  • 1Faculty of Science and Technology, Bournemouth University, Fern Barrow, Poole, BH12 5BB UK.

World Wide Web
|August 9, 2021
PubMed
Summary

Designing AI explanations for human-AI collaboration requires careful consideration of trust calibration. This study identifies errors and proposes five design principles to ensure users appropriately trust AI recommendations in critical decision-making.

Keywords:
Explainable AITrustTrust CalibrationUser Centric AI

More Related Videos

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.3K

Related Experiment Videos

Last Updated: Oct 25, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

783
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.3K

Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence
  • Decision Support Systems

Background:

  • Human-AI collaborative decision-making tools are vital in critical sectors like healthcare.
  • Lack of transparency in these tools hinders user trust and effective collaboration.
  • Explanations are crucial but can lead to trust calibration errors, affecting user judgment.

Purpose of the Study:

  • To explore how explanation interaction design can improve trust calibration in human-AI collaboration.
  • To identify specific trust calibration errors related to AI explainability.
  • To develop design principles for explanations that foster appropriate user trust.

Main Methods:

  • A think-aloud study with 16 participants to uncover trust calibration errors in AI explainability.
  • Two co-design sessions with 8 participants to identify design principles for trust calibration.
  • Qualitative analysis of user interactions and feedback to inform design recommendations.

Main Results:

  • Identified key trust calibration errors, including irrational agreement or disagreement with AI.
  • Developed five core design principles for explanation interaction: engagement, challenging habits, attention guidance, friction, and training/learning.
  • Highlighted the need for intentional design to guide users towards calibrated trust in AI systems.

Conclusions:

  • Explanation interaction design is critical for achieving appropriate trust calibration in human-AI decision-making.
  • The proposed five design principles offer a framework for creating more effective and trustworthy AI explanations.
  • Future work should focus on integrating these principles into a comprehensive framework for AI explanation design.