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Related Experiment Videos

BENN: Bias Estimation Using a Deep Neural Network.

Amit Giloni, Edita Grolman, Tanja Hagemann

    IEEE Transactions on Neural Networks and Learning Systems
    |May 11, 2022
    PubMed
    Summary
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    Bias detection in machine learning (ML) models is complex. BENN, a novel bias estimation method using deep neural networks, offers a unified approach for feature bias analysis, simplifying ethical AI development.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning Ethics
    • Data Science

    Background:

    • Existing bias detection methods in machine learning (ML) present challenges due to varied ethical focuses, incomparable output scales, and complex input requirements.
    • These limitations necessitate human expert intervention, hindering efficient and standardized bias assessment.

    Purpose of the Study:

    • To introduce BENN, a novel bias estimation method designed to overcome the limitations of current approaches.
    • To provide a unified and expert-independent framework for assessing feature-level bias in ML models.

    Main Methods:

    • Development of BENN, a bias estimation method leveraging a pretrained unsupervised deep neural network.
    • BENN analyzes ML model predictions on data samples to generate feature-specific bias estimations.

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    Main Results:

    • BENN was evaluated on benchmark, proprietary, and synthetic datasets, including a churn prediction model.
    • Results demonstrated that BENN's bias estimations align with an ensemble of 21 existing methods.
    • BENN proved to be a generic approach applicable to any ML model without requiring domain expertise.

    Conclusions:

    • BENN offers a significant advancement in bias detection for ML models.
    • The method provides a standardized, comparable, and expert-independent approach to identifying feature bias.
    • BENN facilitates more accessible and reliable ethical AI development and deployment.