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所在平台: Coursera专项课程 课程类别: 其他类别 大学或机构: CourseraNew |
课程主页: https://www.coursera.org/specializations/ethics-in-ai
课程评论:没有评论
课程名称:人工智能时代的伦理学专业化 课程概述:该专业化课程聚焦于人工智能(AI)在现代社会中的伦理问题。学员将深入探讨预测建模、数据偏差、算法及其在人工智能中的伦理应用。课程将涵盖机器学习算法的理解和对预测模型的熟悉,重点分析机器学习中的公平性和伦理考量。 课程大纲: 1. **人工智能算法模型及其限制**:对当今算法主导时代的反思,探讨机器学习模型在决策中的影响。 课程链接:[点击链接](https://www.coursera.org/learn/ai-algorithm-limitations) 2. **人工智能中的数据公平性和偏差**:解析机器学习中的公平性与偏差的基本问题,帮助学员理解如何提高模型的公正性。 课程链接:[点击链接](https://www.coursera.org/learn/ai-data-bias) 3. **人工智能隐私与便利性**:探索机器学习项目中的安全性和隐私相关的基本概念,确保项目的合规性和安全性。 课程链接:[点击链接](https://www.coursera.org/learn/ai-privacy-and-convenience) 4. **人工智能伦理行动**:研究AI伦理学,这是一个新兴领域,学员将通过案例分析来检验自己的批判性思维和应用能力。 课程链接:[点击链接](https://www.coursera.org/learn/ai-ethics-analysis) 此系列课程为希望在人工智能和科技伦理领域深入学习的学员提供了理想的平台,鼓励他们在实际应用中仔细考量伦理影响。
Course Link: https://www.coursera.org/learn/ai-algorithm-limitations
Name:Artificial Intelligence Algorithms Models and Limitations
Description:Offered by LearnQuest. We live in an age increasingly dominated by algorithms. As machine learning models begin making important decisions ... Enroll for free.
Course Link: https://www.coursera.org/learn/ai-data-bias
Name:Artificial Intelligence Data Fairness and Bias
Description:Offered by LearnQuest. In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models ... Enroll for free.
Course Link: https://www.coursera.org/learn/ai-privacy-and-convenience
Name:Artificial Intelligence Privacy and Convenience
Description:Offered by LearnQuest. In this course, we will explore fundamental concepts involved in security and privacy of machine learning projects. ... Enroll for free.
Course Link: https://www.coursera.org/learn/ai-ethics-analysis
Name:Artificial Intelligence Ethics in Action
Description:Offered by LearnQuest. AI Ethics research is an emerging field, and to prove our skills, we need to demonstrate our critical thinking and ... Enroll for free.
Predictive Modellingdata biasAlgorithmsEthics Of Artificial IntelligenceMachine Learning (ML) AlgorithmsUnderstanding of algorithmsFamiliarity with predictive modelsOverview of ethics considersationsmachine learning fairnessEthicsMachine Learningsecurity