Bias in AI & ML: Overview
What is Bias?
- Definition: Results skewed for/against a particular class.
- Source: Patterns from historical data can misrepresent real-world outcomes.
How is Bias Introduced?
Data Issues:
- Misrepresentation: Sensitive features (gender, age) may not be accurately represented.
- Imbalance: Models can be biased if trained on skewed datasets (e.g., more legitimate than fraudulent transactions).
Model Lifecycle:
- Bias can be introduced during training and operations. Continuous monitoring is crucial.
Mitigation Strategies
1. Data Quality & Integrity:
- Ensure diverse, representative datasets.
- Techniques: Under-sampling/over-sampling for balance.
2. Human & Machine Collaboration:
- Use “human in the loop” for critical reviews.
- Amazon Augmented AI (A2I) can facilitate this.
3. Transparency & Explainability:
- Build trust through model explainability.
- AWS tools (e.g., Amazon SageMaker Clarify) can help clarify model decisions.
4. Operational Excellence:
- Develop monitoring, auditing, and reporting strategies.
- Use frameworks like AWS Well-Architected ML lens for best practices.
Tools & Resources
- AWS SageMaker: For data quality, feature engineering, and model monitoring.
- AWS AI Service Cards: Improve transparency and explain use cases.
Key Takeaway
Building responsible AI involves continuous assessment, collaboration between humans and machines, and enhancing transparency.
