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BENN: Bias Estimation Using a Deep Neural Network
Summary
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.
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.