Fairness in Generative AI
A Practical Guide to Benchmarking, Bias Mitigation, and Responsible AI Engineering
Created by Prasanna Vijayanathan
Explore how to build fair and responsible generative AI systems by turning ethical principles into measurable engineering practices. Learn to benchmark, monitor, and govern AI models across text and image domains using practical tools and frameworks. See how real-world fairness issues are addressed from design to deployment.
Packt | Aug 2026 | 310 min
What You Will Learn
You will start by grounding fairness and AI ethics in real-world harms and sensitive attributes. Step by step, you'll define measurable requirements, build modular benchmarks, and use scenario libraries and metrics. Practical exercises guide you through implementing human review, CI gates, drift monitoring, and production controls for responsible AI.
Key Features
- Translate fairness principles into measurable requirements for generative AI systems
- Build and use benchmarks for LLMs, image models, and multimodal AI to assess fairness
- Implement AI governance with human review, CI gates, drift monitoring, and scorecards
Target Audience
Ideal for ML and data engineers, application developers, and responsible AI or trust and safety professionals who want to operationalize fairness in generative AI. Product managers, engineering leaders, and policymakers will also benefit from structured approaches to AI governance. A working knowledge of ML evaluation and Python is recommended.
