ways to improve model robustness:
Data Augmentation
data augmentation can help generalize the model and reduce sensitivity and useful for correcting unbalanced data.
- Ensure training data accurately represents the requests your model will receive.
- Generate data through techniques like generative or interpretive methods, or by adding noise..
Explainable AI (XAI)
- Understand the inner workings of your model to improve robustness.
- Tools and techniques are available to enhance model interpretability.
Model Editing
- Edit models manually to improve performance and robustness.
- Tweak the learned parameters to correct issues and enhance performance.
Model Assertions
- Apply business rules or sanity checks to model results before delivering them.
- Use model assertions to alter or bypass results based on predefined rules.
- Ensure predictions align with domain-specific constraints (for example age can’t be less than 0).
Reducing Model Bias
Diverse Data Set
- Use a diverse and representative data set that reflects the user population.
- Include individuals with diverse backgrounds and expertise in ethics and related disciplines in the development team.
Careful Feature Selection
- Employ feature selection techniques such as sampling and reweighting to minimize discrimination in training data.
- Consider fairness metrics when selecting hyperparameters and decision cutoff thresholds.
Fairness Metrics and Tools
- Utilize fairness indicators and fairness metric evaluation tools.
- Train fair models directly using techniques like learning fair representations (LFR) and adversarial debiasing such as aif360.
- Employ dual objective functions that balance accuracy and fairness metrics.
- Use rejection option classification tools to reduce unwanted bias.
Model Monitoring and Debugging
Continuous Monitoring
- Constantly monitor model accuracy, fairness, and security characteristics.
- Models need regular checks for accuracy, fairness, and security issues as data and circumstances change.
- Implement monitoring into your process to ensure ongoing model performance evaluation.
Anomaly Detection
- Employ techniques such as data integrity constraints, statistical process control, and anomaly detection through autoencoders and isolation forests.
- Compare model predictions to benchmark model predictions to identify anomalies.