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Explore Potential Biases in (AI) Training Data and Strategies to Develop Fair and Unbiased Large Language Models

What you will learn

Introduction To Bias And Fairness In Large Language Models

Types Of Biases In Training Data

Case Studies On Bias In Language Models

Measuring Bias In Language Models

Strategies To Mitigate Bias In Language Models

Ethical Considerations In Developing ChatGPT-Like Models


In the era of powerful AI systems like ChatGPT, it’s crucial to address the issue of bias and ensure the development of fair and inclusive large language models (LLMs). This course provides a comprehensive exploration of the different types of bias that can arise in LLMs, the potential impact of biased outputs, and strategies to mitigate these issues.

You’ll begin by gaining a deep understanding of the various forms of bias that can manifest in LLMs, including historical and societal biases, demographic biases, representational biases, and stereotypical associations. Through real-world examples, you’ll examine how these biases can lead to harmful and discriminatory outputs, perpetuating harmful stereotypes and limiting opportunities for individuals and communities.

Next, you’ll dive into the techniques used to debias the training of LLMs, such as data curation and cleaning, data augmentation, adversarial training, prompting strategies, and fine-tuning on debiased datasets. You’ll learn how to balance the pursuit of fairness with other desirable model attributes, like accuracy and coherence, and explore the algorithmic approaches to incorporating fairness constraints into the training objective.

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Evaluating bias and fairness in LLMs is a complex challenge, and this course equips you with the knowledge to critically assess the various metrics and benchmarks used in this space. You’ll understand the limitations of current evaluation methods and the need for a holistic, multifaceted approach to measuring fairness.

Finally, you’ll explore the real-world considerations and practical implications of deploying fair and unbiased LLMs, including ethical and legal frameworks, continuous monitoring, and the importance of stakeholder engagement and interdisciplinary collaboration.

By the end of this course, you’ll have a comprehensive understanding of bias and fairness in large language models, and the skills to develop more equitable and inclusive AI systems that serve the needs of all individuals and communities.




Intro Video
The Rise of Large Language Models
The Importance of Bias and Fairness
Course Objectives and Overview

Module 1: Understanding Bias in LLMs

Defining Bias in LLMs
Types of Bias in LLMs
Impacts of Biased LLM Outputs

Module 2: Mitigating Bias in LLM Training

Debiasing Techniques
Algorithmic Approaches
Illustrating Debiasing Techniques 1
Illustrating Debiasing Techniques 2

Module 3: Evaluating Bias and Fairness

Measuring Bias and Fairness
Challenges and Considerations
Transitioning to Real-world Considerations

Module 4: Real-world Considerations

Balancing Fairness and Other Attributes
Deployment and Monitoring
Interdisciplinary Collaboration


Key Takeaways
Future Directions and Resources