Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Quality Assurance

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...

Distribution Reliability and Automation

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...

Quality Control

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...

Automatic Processing and Automatic Social Behavior

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

Ethical Standards I

Ethical Standards I

The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...

High-Level and Low-Level Awareness

High-Level and Low-Level Awareness

Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...

You might also read

Related Articles

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

Sort by

Same author

Stacked graph attention network hypertuned by Heteroscedastic and Evolutionary Bayesian Optimization for fault identification on nuclear power plants robust to sensor drift.

Scientific reports·2026

Same author

TriDermCancerNet: A hybrid deep learning framework for skin cancer classification.

The Journal of international medical research·2026

Same author

Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection.

Autonomous intelligent systems·2026

Same author

SDMCC: Sample-wise Debiased Multilevel Contrastive Clustering for Single-cell Gene Expression Data.

IEEE journal of biomedical and health informatics·2026

Same author

A gated task-attentive multi-task network for unified retinal image analysis.

Scientific reports·2026

Same author

Morphology-guided attention networks for explainable skin cancer detection under clinical uncertainty.

Frontiers in oncology·2026

Search research articles

Related Experiment Video

Updated: Mar 18, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Audit-as-code: a policy-as-code framework for continuous AI assurance.

Aoun E Muhammad¹, Kin-Choong Yow¹, Shrooq Alsenan²

¹Faculty of Engineering and Applied Science, University of Regina, Regina, SK, Canada.

Frontiers in Artificial Intelligence

|March 16, 2026

Summary

This summary is machine-generated.

Audit-as-Code transforms AI assurance by mapping governance requirements to automated, verifiable controls. This framework operationalizes compliance, enabling continuous monitoring and auditable AI systems.

Keywords:

AI assurance CI/CD compliance explainability governance policy-as-code reproducibility traceability

Related Experiment Videos

Last Updated: Mar 18, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Area of Science:

Artificial Intelligence
Software Engineering
Information Assurance

Background:

Current AI governance relies on manual policy reviews, hindering scalable and reproducible compliance.
Operationalizing qualitative AI requirements into verifiable controls presents a significant challenge for continuous assurance.

Purpose of the Study:

To introduce Audit-as-Code, a continuous assurance framework for AI systems.
To operationalize AI governance and compliance within MLOps/CI-CD workflows.
To develop an assured readiness score for automated deployment decisions.

Main Methods:

Developed Audit-as-Code, mapping governance to auditable rules and executable checks.
Integrated versioned policy specifications and evidence artifact checks.
Created an assured readiness score incorporating governance risk, traceability, and explainability.

Main Results:

Demonstrated Audit-as-Code's effectiveness on representative AI systems.
Showcased the reusability of evidence bundles across diverse governance regulations.
Validated the framework's ability to automate compliance decisions and provide targeted improvement suggestions.

Conclusions:

Audit-as-Code shifts AI assurance from documentation-centric to a quantitative, auditable, and practical approach.
The framework enhances the reproducibility and scalability of AI compliance.
Enables automated, risk-tiered deployment decisions for AI systems.