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

Decision Making

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Machines

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

Machines: Problem Solving II

Machines: Problem Solving II

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.

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...

You might also read

Related Articles

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

Sort by

Same author

An ECG biomarker for sudden cardiac death discovered with deep learning.

Nature·2026

Same author

A reporting checklist for large language models in behavioural science.

Nature human behaviour·2026

Same author

Bio-BLIP: A Multimodal Architecture for Transferable Reasoning in Genomic Variant Interpretation.

bioRxiv : the preprint server for biology·2026

Same author

Social cognition and interpersonal violence.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

PULSAR: a Foundation Model for Multi-scale and Multi-cellular Biology.

bioRxiv : the preprint server for biology·2025

Same author

Autonomous AI Agents Discover Aging Interventions from Millions of Molecular Profiles.

bioRxiv : the preprint server for biology·2025

Same journal

LIVES VERSUS LIVELIHOODS: THE IMPACT OF THE GREAT RECESSION ON MORTALITY AND WELFARE.

The quarterly journal of economics·2026

Same journal

What Drives Risky Prescription Opioid Use?: Evidence from Migration.

The quarterly journal of economics·2025

Same journal

DIAGNOSING PHYSICIAN ERROR: A MACHINE LEARNING APPROACH TO LOW-VALUE HEALTH CARE.

The quarterly journal of economics·2025

Same journal

THE IMPACT OF BEING DENIED A WANTED ABORTION ON WOMEN AND THEIR CHILDREN.

The quarterly journal of economics·2025

Same journal

Crowding in Private Quality: The Equilibrium Effects of Public Spending in Education.

The quarterly journal of economics·2025

Same journal

MORE POWER TO THE PILL: THE IMPACT OF CONTRACEPTIVE FREEDOM ON WOMEN'S LIFE CYCLE LABOR SUPPLY.

The quarterly journal of economics·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 10, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

HUMAN DECISIONS AND MACHINE PREDICTIONS.

Jon Kleinberg¹, Himabindu Lakkaraju², Jure Leskovec²

¹Cornell University.

The Quarterly Journal of Economics

|May 15, 2018

Summary

This summary is machine-generated.

Machine learning can significantly improve judicial decision-making in bail cases, potentially reducing crime by up to 24.7% without increasing jailing rates. This technology also shows promise in reducing racial disparities in the legal system.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Functional Human Liver Preservation and Recovery by Means of Subnormothermic Machine Perfusion

Functional Human Liver Preservation and Recovery by Means of Subnormothermic Machine Perfusion

Published on: April 27, 2015

Related Experiment Videos

Last Updated: Feb 10, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Functional Human Liver Preservation and Recovery by Means of Subnormothermic Machine Perfusion

Functional Human Liver Preservation and Recovery by Means of Subnormothermic Machine Perfusion

Published on: April 27, 2015

Area of Science:

Computational Social Science
Economics
Legal Studies

Background:

Judicial bail decisions involve predicting defendant behavior, a task suitable for machine learning.
Existing data limitations and judicial preferences complicate algorithm-to-judge comparisons.

Purpose of the Study:

To evaluate the potential of machine learning to enhance human decision-making in the context of bail.
To address challenges in comparing algorithmic predictions with judicial decisions and preferences.

Main Methods:

Utilized econometric strategies, including quasi-random assignment, to overcome data limitations.
Developed policy simulations to assess welfare gains from algorithmic integration.

Main Results:

Simulations indicate potential crime reductions up to 24.7% without altering jailing rates.
Demonstrated possibility of reducing jailing rates by 41.9% without increasing crime.
Observed reductions across all crime categories, including violent crimes, and decreased racial disparities.

Conclusions:

Machine learning offers substantial welfare gains in judicial decision-making.
Realizing machine learning's value requires an economic framework, clear prediction-decision links, defined payoff functions, and unbiased counterfactuals.