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Foundations of Responsible AI

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2:30:38

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  • 01 - Understanding responsible AI.mp4
    01:09
  • 01 - What is AI and how does data enable it.mp4
    03:01
  • 02 - Modern AI development.mp4
    06:14
  • 03 - Problems in ML that differ from software engineering.mp4
    06:20
  • 01 - Big data and where it comes from.mp4
    05:24
  • 02 - Seeing trends in data.mp4
    05:09
  • 03 - Building data understanding.mp4
    02:10
  • 04 - Visualization and comparing data.mp4
    03:16
  • 05 - Storytelling with data.mp4
    05:35
  • 01 - Introduction to ethical AI.mp4
    03:38
  • 02 - Ethical frameworks.mp4
    05:51
  • 03 - Beneficence vs. maleficence.mp4
    04:31
  • 04 - Calculating consequences.mp4
    03:59
  • 05 - Consequence scanning.mp4
    03:20
  • 06 - Common good and equity.mp4
    06:05
  • 01 - Fairness.mp4
    04:08
  • 02 - Transparency.mp4
    04:35
  • 03 - Accountability.mp4
    05:17
  • 04 - Explanations.mp4
    04:04
  • 05 - Interpretability.mp4
    03:57
  • 06 - Inclusivity.mp4
    04:04
  • 01 - Why fairness related harms.mp4
    04:29
  • 02 - Critical AI incidents and learnings.mp4
    05:12
  • 03 - Bias in the design and development lifecycle.mp4
    02:43
  • 04 - Causal reasoning and fairness.mp4
    04:13
  • 05 - Risk mitigation in AI.mp4
    05:41
  • 06 - Technical aspects of sociotechnical solutions.mp4
    06:24
  • 01 - Anonymity and data privacy.mp4
    04:38
  • 02 - Unintended uses and misuses.mp4
    04:43
  • 03 - Unethical business cases.mp4
    05:30
  • 04 - Autonomous systems and society.mp4
    04:54
  • 05 - Who AI is developed for.mp4
    06:51
  • 01 - AI regulation and applying responsible AI frameworks.mp4
    03:33
  • Description


    How well do you understand the issues around algorithmic bias and unfairness? In this course, data scientist and AI ethicist Ayodele Odubela teaches you the principles of responsible AI, as well as the frameworks necessary to apply RAI techniques in AI systems. Ayodele explains modern AI development and problems in machine learning (ML) that differ from software engineering. She discusses big data and where it comes from, then covers several important points of data awareness and literacy. Ayodele goes over ethical frameworks, consequence scanning, fairness, accountability, and more. She also explains fairness-related harms, why they arise, why they matter, and what you can to avoid or mitigate them. Plus, Ayodele offers a detailed description of human rights as related to AI.

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    LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.
    • language english
    • Training sessions 33
    • duration 2:30:38
    • Release Date 2023/01/09