Free Download Bias And Fairness In Large Language Models
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.29 GB | Duration: 0h 43m
Explore Potential Biases in (AI) Training Data and Strategies to Develop Fair and Unbiased Large Language Models
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.
Overview
Section 1: Introduction
Lecture 1 Intro Video
Lecture 2 The Rise of Large Language Models
Lecture 3 The Importance of Bias and Fairness
Lecture 4 Course Objectives and Overview
Section 2: Module 1: Understanding Bias in LLMs
Lecture 5 Defining Bias in LLMs
Lecture 6 Types of Bias in LLMs
Lecture 7 Impacts of Biased LLM Outputs
Section 3: Module 2: Mitigating Bias in LLM Training
Lecture 8 Debiasing Techniques
Lecture 9 Algorithmic Approaches
Lecture 10 Illustrating Debiasing Techniques 1
Lecture 11 Illustrating Debiasing Techniques 2
Section 4: Module 3: Evaluating Bias and Fairness
Lecture 12 Measuring Bias and Fairness
Lecture 13 Challenges and Considerations
Lecture 14 Transitioning to Real-world Considerations
Section 5: Module 4: Real-world Considerations
Lecture 15 Balancing Fairness and Other Attributes
Lecture 16 Deployment and Monitoring
Lecture 17 Interdisciplinary Collaboration
Section 6: Conclusion
Lecture 18 Key Takeaways
Lecture 19 Future Directions and Resources
Who is this course for? This course is suitable for a wide range of learners, including: Data scientists, machine learning engineers, and AI researchers who want to develop a deeper understanding of bias and fairness issues in large language models. Product managers, UX designers, and business leaders who work with or deploy AI-powered chatbots and conversational interfaces. Ethics and policy professionals interested in the societal implications of biased AI systems. Computer science students and anyone curious about the current challenges and best practices in building fair and inclusive AI.,The course aims to be accessible and valuable for learners from diverse backgrounds, with no prior expertise in AI or machine learning required. Through clear explanations, practical examples, and hands-on exercises, participants will gain the knowledge and skills to identify, mitigate, and evaluate bias in large language models.
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