Here is a detailed summary of the video transcription in markdown format, broken down into sections for better readability:
Understanding Bias
- Bias definition: An inclination of unreasoned judgment, a statistical estimate or deviation, and a systematic error.
- Common fairness metrics: Equal opportunity, equalized odds, and demographic parity.
- Example of bias in facial recognition: Gender Shades study showed significant misclassification of gender, especially for darker-skinned females.
Bias in Machine Learning Life Cycle
- Data processing: Ensuring diverse and representative data to avoid biased models.
- Challenges with large language models (LLMs):
- Opaque data sources and model architectures.
- Reliance on transfer learning from potentially biased "parent" models.
- Cultural and contextual awareness issues when deploying LLMs globally.
Bias in Model Development
- Model architecture, hyperparameter tuning, and objective/loss functions can introduce bias.
- Importance of keeping humans in the loop for monitoring and mitigation.
Strategies for Bias Mitigation
For Traditional Machine Learning Models
- Model monitoring for concept drift, data drift, and model drift.
- Fairness audits and user feedback/reporting mechanisms.
For Large Language Models
- Adversarial testing to identify vulnerabilities.
- Using Amazon Bedrock guardrails and retrieval-augmented generation (RAG) models.
Industry Resources
- Anthropic's "Constitutional AI" approach.
- Stanford's "Human-Centered AI Index" chapter on responsible AI.
- AWS Science Blog for publications on bias mitigation.