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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

195
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...
195
Halo Effect01:27

Halo Effect

207
The halo effect is a cognitive bias in which an individual's overall impression influences judgments about their specific traits. This psychological phenomenon leads people to associate positive characteristics with those they perceive as generally good and negative characteristics with those they view as bad. This effect is particularly influential in social perception, professional evaluations, and decision-making processes.The Psychological Basis of the Halo EffectThe halo effect is rooted...
207
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

8.0K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
8.0K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

283
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
283
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

387
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
387
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

247
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
247

You might also read

Related Articles

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

Sort by
Same author

Harmonized geospatial data to evaluate the Electric Distribution Networks in the US Northeast.

Scientific data·2025
Same author

Structure matters: Assessing the statistical significance of network topologies.

PloS one·2024
Same author

Machine Learning-Based Surrogate Model for Press Hardening Process of 22MnB5 Sheet Steel Simulation in Industry 4.0.

Materials (Basel, Switzerland)·2022
Same author

Modeling financial distress propagation on customer-supplier networks.

Chaos (Woodbury, N.Y.)·2021
Same author

Differential Replication for Credit Scoring in Regulated Environments.

Entropy (Basel, Switzerland)·2021
Same author

The Challenges of Machine Learning and Their Economic Implications.

Entropy (Basel, Switzerland)·2021
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Dec 2, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Risk mitigation in algorithmic accountability: The role of machine learning copies.

Irene Unceta1,2, Jordi Nin3, Oriol Pujol2

  • 1BBVA Data & Analytics, Barcelona, Spain.

Plos One
|November 3, 2020
PubMed
Summary
This summary is machine-generated.

Copies can mitigate risks in complex machine learning systems when models cannot be retrained. This approach offers actionable accountability for machine learning (ML) 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

11.0K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K

Related Experiment Videos

Last Updated: Dec 2, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.0K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Machine learning (ML) is a key driver of economic growth and efficiency.
  • ML systems often integrate third-party components and APIs, increasing complexity.
  • A significant challenge in ML is the lack of actionable accountability guidance.

Purpose of the Study:

  • To investigate the role of copies in mitigating risks within complex ML systems.
  • To explore the use of copies as an alternative for risk management when model retraining is not feasible.
  • To provide actionable accountability guidance for ML systems.

Main Methods:

  • Formally defining a copy as an approximated projection operator.
  • Utilizing a conceptual framework of actionable accountability.
  • Applying the approach to a real-world residential mortgage default dataset.

Main Results:

  • Demonstrated the feasibility of using copies for risk mitigation in ML systems.
  • Showcased the practical application of copies when model retraining or wrappers are not viable.
  • Provided a viable alternative for enhancing accountability in ML.

Conclusions:

  • Copies serve as a practical tool for risk mitigation in complex machine learning systems.
  • The proposed method offers a viable solution for enhancing actionable accountability.
  • The approach is effective even when direct model modification is not possible.