Foundations of Responsible AI

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2:30:38

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01 - Introduction

01 - Understanding responsible AI.mp4

01:09

02 - 1. Philosophy of AI

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

03 - 2. Data Awareness and Literacy

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

04 - 3. Ethical Theories

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

05 - 4. Responsible AI Principles

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

06 - 5. Algorithmic Harm

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

07 - 6. Human Rights and AI

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

08 - Conclusion

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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