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Ethical AI and Algorithmic Fairnes
1 - Bias Mitigation Strategies
- Diverse Datasets: Include varied demographics in training data.
- Continuous Updating: Re-audit for bias as user populations evolve.
- Algorithm Testing: Implement fairness metrics like disparate impact analysis.
2 - Transparent Algorithms
- Interpretable Models: Use methods yielding traceable decision paths.
- Algorithm Documentation: Maintain logs of version changes or hyperparameters.
- Public Access: Offer user-facing, plain-language descriptions of how key algorithms work.