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Related Concept Videos

Bias01:22

Bias

4.3K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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The Representativeness Heuristic02:13

The Representativeness Heuristic

15.9K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Biasing of P-N Junction01:16

Biasing of P-N Junction

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The operation of a p-n junction diode involves various biasing conditions, including forward bias, reverse bias, and equilibrium.
In equilibrium, no external voltage is applied across the p-n junction. The depletion region is formed at the junction interface due to the diffusion of carriers, which leaves behind charged dopants, acceptors on the p-side, and donors on the n-side. These immobile charges create an electric field that prevents further diffusion of carriers. The related energy band...
638
Biasing of FET01:22

Biasing of FET

333
Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
In an N-channel JFET, the structure consists of N-type material forming the channel on a P-type substrate, with the...
333
Biasing of Metal-Semiconductor Junctions01:27

Biasing of Metal-Semiconductor Junctions

290
Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...
290
Confirmation Biases01:31

Confirmation Biases

5.5K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Updated: Aug 2, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Demystifying and Mitigating Bias for Node Representation Learning.

O Deniz Kose, Yanning Shen

    IEEE Transactions on Neural Networks and Learning Systems
    |April 17, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study explains bias in graph neural networks (GNNs) and introduces fairness-aware data augmentation to reduce it. Experiments show these methods improve fairness in node classification and link prediction.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Graph Neural Networks

    Background:

    • Node representation learning is crucial for graph data analysis.
    • Fairness in graph neural networks (GNNs) is under-explored, yet graph structure amplifies bias.
    • Existing methods lack theoretical understanding of bias sources.

    Purpose of the Study:

    • To theoretically explain bias origins in GNN node representations.
    • To develop fairness-aware data augmentation frameworks.
    • To mitigate bias while maintaining model utility.

    Main Methods:

    • Theoretical analysis of bias in GNNs, considering nodal features and graph structure.
    • Development of fairness-aware data augmentation techniques.
    • Empirical evaluation on node classification and link prediction tasks.

    Main Results:

    • Identified both nodal features and graph structure as sources of bias.
    • Proposed augmentation strategies effectively reduce bias in node representations.
    • Achieved improved fairness with comparable utility to existing methods.

    Conclusions:

    • Bias in GNNs stems from both data features and network topology.
    • Fairness-aware data augmentation is a viable strategy for mitigating bias.
    • The proposed methods offer a practical approach for fairer graph learning.