Bias and Fairness in 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.
Who this course is for:
- 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.
Instructor
- 4.3 Instructor Rating
- 22 Reviews
- 3,815 Students
- 3 Courses
Tolulope Ojo, the visionary force behind Tetolis Digital Graphics Plus, is a seasoned professional at the forefront of shaping the future in the creative industry. Boasting certifications from esteemed institutions such as:
1. Florida Atlantic University and
2. The Project Management Institute,
Tolulope's expertise transcends traditional boundaries.
As a versatile instructor, Tolulope dedicates himself to empowering a diverse audience, including teenagers, youths, graduates, and undergraduates, regardless of employment status. His mission is clear—to combat unemployment by equipping the next generation with valuable skills and unlocking future opportunities. Tolulope's extensive certifications, range from the:
1. TRANSFORMING NIGERIAN YOUTHS PROGRAM at Enterprise Development Center (EDC), Pan Atlantic University, and some other professional institutions to:
2. Graphics Design insights from LinkedIn Learning, uniquely position him to bridge the gap between education and industry demands.
Tolulope's commitment to democratizing knowledge and fostering skill development has left a lasting impact on thousands. His training programs transcend the ordinary, nurturing growth and preparing individuals for success in the ever-evolving creative industries. Whether you're seeking insights into industry trends, exploring the integration of AI across diverse sectors, or aiming to maximize your potential, Tolulope Ojo is the experienced guide you need. Connect with him to discover how his expertise can elevate your journey across the expansive landscape of creative and tech-driven opportunities. Unleash your potential with Tolulope Ojo as your mentor, opening doors to a future teeming with possibilities.