What you'll 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

Requirements
Here are the requirements and prerequisites for the "Bias and Fairness in Large Language Models" Udemy course: Prerequisites: No prior experience with large language models or AI ethics is required. This course is designed for learners at all levels, from beginners to experienced AI practitioners. A basic understanding of machine learning and natural language processing concepts would be helpful, but not strictly necessary. The course will provide explanations and introductions to these topics as needed. Familiarity with using online tools and platforms for learning and research purposes.

Description
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.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.