Trust calibration for a human-machine team is the process by which a human adjusts their expectations of the automation's reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automation. Herein, we leverage an instance-based learning ACT-R cognitive model of decisions to obtain and rely on an automated assistant for visual search in a UAV interface. This cognitive model matches well with the human predictive power statistics measuring reliance decisions; we obtain from the model an internal estimate of automation reliability that mirrors human subjective ratings. The model is able to predict the effect of various potential disruptions, such as environmental changes or particular classes of adversarial intrusions on human trust in automation. Finally, we consider the use of model predictions to improve automation transparency that account for human cognitive biases in order to optimize the bidirectional interaction between human and machine through supporting trust calibration. The implications of our findings for the design of reliable and trustworthy automation are discussed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8181412 | PMC |
| http://dx.doi.org/10.3389/frobt.2021.652776 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Risk prediction tools for pressure injury occurrence: an umbrella review of systematic reviews reporting model development and validation methods.
Diagn Progn Res
January 2025
Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Background: Pressure injuries (PIs) place a substantial burden on healthcare systems worldwide. Risk stratification of those who are at risk of developing PIs allows preventive interventions to be focused on patients who are at the highest risk. The considerable number of risk assessment scales and prediction models available underscores the need for a thorough evaluation of their development, validation, and clinical utility.
View Article and Find Full Text PDFA constrained optimisation framework for parameter identification of the SIRD model.
Math Biosci
January 2025
Maxwell Institute for Mathematical Sciences, The University of Edinburgh and Heriot-Watt University, Bayes Centre, Edinburgh, Scotland, UK; School of Mathematics, The University of Edinburgh, James Clerk Maxwell Building, Edinburgh, Scotland, UK. Electronic address:
We consider a numerical framework tailored to identifying optimal parameters in the context of modelling disease propagation. Our focus is on understanding the behaviour of optimisation algorithms for such problems, where the dynamics are described by a system of ordinary differential equations associated with the epidemiological SIRD model. Applying an optimise-then-discretise approach, we examine properties of the solution operator and determine existence of optimal parameters for the problem considered.
View Article and Find Full Text PDFStormwater controls for channel stability: Focusing on bed material transport prevents degradation.
J Environ Manage
January 2025
Biological Systems Engineering, Hampton Roads Agricultural Research and Extension Center, Virginia Tech, Virginia Beach, VA, USA.
The hydrologic benefits of catchment-scale implementation of stormwater control measures (SCMs) in mitigating the adverse effects of urbanization are well established. Nevertheless, recent studies indicate that the Unified Stormwater Sizing Criteria (USSC) regulations, mandating the combined use of distributed and storage stormwater controls, do not protect channel stability, despite their effectiveness in reducing runoff from impervious surfaces. The USSC are the basis of SCM design in 11 U.
View Article and Find Full Text PDFIn-depth Computational Analysis Reveals The Significant Dysregulation of Key Gap Junction Proteins (GJPs) Driving Thoracic Aortic Aneurysm Development.
Hellenic J Cardiol
January 2025
Department of Cardiac Surgery Research, Lankenau Institute for Medical Research, Main Line Health, Wynnewood, PA, 19096, USA; Department of Cardiac Surgery, Lankenau Heart Institute, Main Line Health, Wynnewood, PA, 19096, USA. Electronic address:
Objective: Thoracic Aortic Aneurysm (TAA) represents an aortic pathology that is caused by the deranged integrity of the three layers of the aortic wall, and is related to severe morbidity and mortality. Consequently, it is crucial to identify the biomarkers implicated in the pathogenesis and biology of TAA. The aim of the current computational study was to assess the differential gene expression profile of the gap junction proteins (GJPs) in patients with TAA in order to identify novel potential biomarkers for the diagnosis and treatment of this disease.
View Article and Find Full Text PDFWhole examination AI estimation of fetal biometrics from 20-week ultrasound scans.
NPJ Digit Med
January 2025
School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by aggregating automatically extracted biometrics from every frame across an entire scan, with no need for operator intervention. We use a neural network to classify each frame of an ultrasound video recording.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!