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

Introduction to Learning01:18

Introduction to Learning

637
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
637
Law of Effect01:06

Law of Effect

1.9K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
1.9K
Machines: Problem Solving II01:30

Machines: Problem Solving II

459
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
459
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

135
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
135
Machines: Problem Solving I01:22

Machines: Problem Solving I

493
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
493
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.4K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.4K

You might also read

Related Articles

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

Sort by
Same author

Relating Model Performance to Embedding Distributions in Molecular Machine Learning.

Journal of chemical information and modeling·2026
Same author

Potential Pitfalls With Automatic Sentiment Analysis: The Example of Queerphobic Bias.

Social science computer review·2023
Same author

Logic + probabilistic programming + causal laws.

Royal Society open science·2023
Same author

Breaking CAPTCHA with Capsule Networks.

Neural networks : the official journal of the International Neural Network Society·2022
Same author

Signal Perceptron: On the Identifiability of Boolean Function Spaces and Beyond.

Frontiers in artificial intelligence·2022
Same author

Intention Recognition With ProbLog.

Frontiers in artificial intelligence·2022
Same journal

Deep learning model to predict COPD hospital admissions based on meteorological data: a medical meteorological forecast.

Frontiers in big data·2026
Same journal

Where diverse populations gather: transit accessibility and the spatial structure of social mixing.

Frontiers in big data·2026
Same journal

Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context.

Frontiers in big data·2026
Same journal

Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models.

Frontiers in big data·2026
Same journal

Using artificial intelligence to improve governance and public services in Africa.

Frontiers in big data·2026
Same journal

Case count metric for comparative analysis of entity resolution results.

Frontiers in big data·2026
See all related articles

Related Experiment Video

Updated: Oct 27, 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

785

Principles and Practice of Explainable Machine Learning.

Vaishak Belle1,2, Ioannis Papantonis1

  • 1School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

Frontiers in Big Data
|July 19, 2021
PubMed
Summary
This summary is machine-generated.

Explainable machine learning (ML) helps build trust in AI systems by clarifying decision-making processes. This survey guides practitioners in selecting appropriate tools for understanding complex models and mitigating biases.

Failed At:

2026-06-19T13:39:06.353355+00:00

Keywords:
black-box modelsexplainable AImachine learningsurveytransparent models

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K

Related Experiment Videos

Last Updated: Oct 27, 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

785
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K