Building Fairness and unbiased GenAI

About this Video

• Video Title: Building Fairness and Unbiased GenAI
• Channel: Gooru Content
• Speakers: (Not specified in transcript)
• Duration: 00:03:58

Introduction

This video discusses the importance of building fairness into generative AI systems. It explains how bias in AI can perpetuate harmful stereotypes and explores methods for identifying, measuring, and mitigating bias throughout the AI lifecycle, advocating for inclusive development processes.

Key Takeaways

• Bias in Generative AI: Generative AI systems can amplify existing societal biases, leading to harmful outputs and real-world societal impact. Bias can manifest as representation bias, allocation bias, quality of service bias, and stereotyping bias.
• Structured Fairness Assessment: A structured approach to fairness assessment is crucial. This involves identifying stakeholders, mapping potential harms, defining quantifiable fairness metrics, rigorous testing and validation, and establishing continuous monitoring and improvement processes.
• Fairness Metrics: Various fairness metrics exist, including demographic parity, equal opportunity, predictive parity, and equalized odds, each addressing different aspects of bias. The choice of metric depends on the specific context and cost of false positives/negatives.
• Bias Mitigation Techniques: Bias mitigation techniques can be applied at different stages of the AI lifecycle: pre-processing (data cleaning), in-processing (modifying the model training), and post-processing (adjusting model output). A combination of approaches is often most effective.
• Inclusive Development Process: Building fair AI requires an inclusive development process, including diverse teams, stakeholder consultation, participatory design, and robust feedback loops.