Mitigating Bias and Ensuring Fairness in GenAI Systems

Mitigating Bias and Ensuring Fairness in GenAI Systems in English!

Disclaimer- This course contains the use of artificial intelligence

Generative AI is transforming industries—but with great power comes a critical responsibility: ensuring fairness and reducing bias in AI outputs.

In this course, you’ll get a practical, beginner-friendly introduction to the key concepts, challenges, and solutions for building responsible AI systems.

What you’ll learn:

• What Generative AI (GenAI) is and how it works

• The differences between GenAI and Large Language Models (LLMs)

• Overview of popular GenAI platforms and tools

• Familiar sources of bias in AI models

• Proven strategies to mitigate bias and ensure fairness

• A brief introduction to Retrieval-Augmented Generation (RAG) and how it improves context accuracy

I am Minerva Singh. I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science-intensive PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience developing AI applications for a variety of UK universities and organisations, producing publications for international peer-reviewed journals and undertaking data science consultancy work. In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to ensure you get the most value out of your investment!

ENROLL NOW :)

Whether you’re a developer, analyst, AI enthusiast, or business leader, this course will equip you with the knowledge to identify, reduce, and manage bias in AI systems—and to create AI solutions that are transparent, ethical, and trustworthy.

By the end, you’ll have a clear action plan to integrate fairness into your GenAI workflows, no matter which platform or model you’re using.

No advanced technical background required—just curiosity and a commitment to building better AI.