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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/wharton-ai-strategy-governance
课程评论：没有评论
课程名称：人工智能战略与治理 课程概述：本课程将帮助您探索人工智能（AI）及其在企业转型中带来的竞争优势。课程涵盖了AI在企业设置中的多种用途及其降低使用障碍的工具。您将深入了解可解释性AI的目的、功能和应用案例。此外，课程还将提供构建负责任的AI治理算法的工具，教授如何处理大规模数据集以及其对业务的影响。最后，您将研究AI在组织结构中的作用、变更管理中的重要性及AI过程所面临的风险。课程结束时，您将学会识别数据中的偏见、确保用户数据和隐私的信任，以及构建负责任治理战略的要素。 课程大纲： 模块1 - AI经济学：深入分析AI的关键输入、降低AI使用门槛的工具、AI的经济学及其对行业竞争的影响，了解数据与深度学习的价值及计算硬件的复杂性。 模块2 - AI创新：探讨AI与数据分析的经济应用案例，分析大数据的例子及其对生产力与转型的推动作用，重点关注AI在生物制药行业的应用。 模块3 - 算法偏见与公平性：研究数据中的固有偏见及算法偏见问题，探讨如何解决这些挑战及数据保护的法律框架，确保算法的公正性。 模块4 - AI治理与可解释AI：学习可解释AI及其与深度学习的关系，探讨公平算法及政策的重要性，理解建立信任的伦理原则与治理政策。 最终，通过本课程，您将全面掌握AI治理的策略与实施方法，为在企业内推动创新与变革提供有力的支持。
Name:Module 1 – Economics of AI
Description:In this module, you will begin by examining the key inputs to AI and what tools are currently used to lower the barriers of entry for AI use. Next, you will learn the economics of AI and the competition that has emerged as AI becomes more crucial to support industry needs and we see more cloud adoption. You will learn about the value of data as it is tied to Deep Learning, and how AutoML is changing the landscape of Machine Learning, and the growing competition and implications of data harvesting. By the end of this module, you will have gained knowledge about the economic implications of AI and Machine Learning and how they impact our lives in unseen ways. You will also understand the complex nature of computational hardware and how that affects consumer demand, but also the demand for privacy.
Name:Module 2 – AI Innovation
Description:In this module, you will examine AI and data analytics to show the economical use-cases of Big Data. You will also learn about the methods and tools that are being used to lower the barriers of entry for AI use. You will review current examples of Big Data and how those firms are using their analytical tools to enhance productivity and transformation. Lastly, you will get an in-depth look at how AI can be used in BioPharma and how the payoff of their AI investment is revitalizing their industry. By the end of this module, you will have a firm grasp on the practical deployment of AI across different industries, their use-cases, and how you can best implement them to drive innovation and transformation within business.
Name:Module 3 – Algorithmic Bias and Fairness
Description:In this module, you will examine the inherent bias that can exist within data based on human behaviors. Building on these foundations, you will explore different responses within algorithmic bias and how organizations should respond and overcome these challenges. You will then review the manipulation of data, the different kinds of manipulation, and ways to ethically approach these issues. Lastly, you will examine data protection and the legal frameworks that exist to protect the consumer and individual data, and the stages of the privacy lifecycle. By the end of this module, you will have a thorough understanding of data biases, manipulation, and ethical questions of how data is handled and stored. You will be able to implement fairer algorithms and understand the legal ramifications of improperly managing data you collect.
Name:Module 4 – AI Governance and Explainable AI
Description:In this module, you will learn about explainable AI and its relationship to Deep Learning. You will also review why it is important to have explainable AI and the different approaches to creating fair algorithms and AI policies. You will also examine Explainable AI and review the necessity of equitable algorithms. You will also learn why we do not always use Explainable AI for every model, and the impacts that it can have on performance. By the end of this module, you will have gained insight into decision-making with AI and the importance of fairness and transparency in creating explainable AI systems, as well as the ethical principles and governance policies that build trust in using AI and Machine Learning.
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
