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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ethical-ai-development-course/
课程评论：没有评论
课程名称：大型语言模型中的偏见与公平性 概述：在ChatGPT等强大人工智能系统时期，解决偏见问题并确保大型语言模型（LLMs）的公平性和包容性至关重要。本课程全面探讨了LLMs中可能出现的不同类型偏见、偏见输出的潜在影响以及缓解这些问题的策略。课程开篇将深入理解LLMs中可能表现出的各种偏见形式，包括历史和社会偏见、人口统计偏见、代表性偏见以及刻板印象。通过现实案例，学员将探讨这些偏见如何导致有害和歧视性的输出，延续有害的刻板印象，限制个人和社区的机会。 接下来，课程将深入介绍去偏见技术，例如数据筛选和清理、数据增强、对抗性训练、提示策略和在去偏见数据集上的微调。学员将学习如何在追求公平与其他期望的模型属性（如准确性和连贯性）之间取得平衡，以及探索将公平约束纳入训练目标的算法方法。 评估LLMs中的偏见与公平性是一个复杂的挑战，本课程将使您掌握批判性评估这一领域所用的各种指标和基准的知识。学员将理解当前评估方法的局限性，以及需要采取整体、多方面的方法来衡量公平性。 最后，课程将探讨部署公平和无偏见的LLMs的现实考虑与实际影响，包括伦理和法律框架、持续监测以及利益相关者参与及跨学科合作的重要性。 通过此课程，学员将全面了解大型语言模型中的偏见与公平性，并掌握开发更公平和包容的AI系统的技能，以满足所有个人和社区的需求。
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.
