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Bias and Fairness in Large Language Models

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Description

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

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.

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