Strategies to Mitigate Social Bias when Implementing Gen Workloads
Introduction
- Lisa and Monica, experts in technical program management and security consulting, discuss the challenges associated with Gen AI workloads and strategies to mitigate bias throughout the development process.
- They explore features such as Amazon Bedrock guardrails, Amazon Sagemaker Clarify, and Sagemaker Data Wrangler to help identify and mitigate biases in machine learning models and data.
Types of Biases in Gen AI Systems
- Sample Bias: Occurs when the training data used to develop the model is not representative of the diverse population the AI system is trying to interact with, leading to biased outputs.
- Historical Biases: Manifest when the training data reflects societal biases and discriminatory practices from the past, perpetuating or amplifying these biases in the AI system.
- Measurement Bias: Arises when the evaluation metrics used to assess the performance of Gen AI systems fail to capture important aspects of fairness, leading to biased systems being deemed successful while overlooking their potential for discrimination.
Impact of Bias on Society
Biases in Gen AI systems can lead to:
- Perpetuation of harmful stereotypes and discrimination
- Spread of misinformation and false narratives
- Polarization within society by prioritizing certain perspectives while suppressing others
Strategies for Bias Detection and Mitigation
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Sourcing Training Data Broadly and Responsibly:
- Involve subject matter experts to ensure data representation
- Include data from diverse sources, including non-English language contexts
- Utilize automated services like Amazon Sagemaker Data Wrangler to assess data quality and balance.
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Interdisciplinary Collaboration:
- Involve professionals from various backgrounds, such as machine learning, cybersecurity, ethics, and law, to design and develop Gen AI workloads.
- Establish an AI governance strategy to guide the use of Gen tools in the organization.
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Conducting Model Evaluation:
- Leverage Amazon Sagemaker Clarify to evaluate models for bias, both through automated and human-centered evaluation methods.
- Monitor production models for bias drift using Amazon Sagemaker Model Monitor.
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Filtering Output to Mitigate Discriminatory and Biased Content:
- Utilize Amazon Bedrock guardrails to set up preventative controls, such as topic denial, word filtering, content filtering, and PII reduction, to ensure safe and inclusive Gen AI outputs.
Conclusion
- Lisa and Monica emphasize the importance of addressing and mitigating biases in Gen AI systems to promote equality, foster inclusivity, and create a more equitable society.
- They provide QR codes and links to help participants get started with the discussed services and strategies.