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Ai Ethics And Governance: Concepts And Applications
Published 1/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 536.93 MB | Duration: 2h 46m
Designing, Deploying, and Governing Ethical AI Systems | Responsible AI Deployments for the Real World
What you'll learn
Need for ethics and governance in AI
What are the different frameworks for AI Ethics
How to implement AI Ethics framework in your organization
What are FEAT metricss
What is explainable AI
What are the different types of bias
How sampling drives many issues in ethics and governance areas
Requirements
Basic knowledge of AI will be advantageous
Few years of working in an organization would also be advantageous
Description
AI Ethics and Governance: Concepts and ApplicationsImagine a self driving car misreading a road sign, causing a collision or a loan application system that routinely rejects applicants from certain backgrounds due to biased training data. From facial recognition software leading to wrongful detentions, to AI based diagnoses that overlook critical symptoms, the stakes are high when AI goes wrong. These real world examples highlight why ethics must be integral to any AI initiative.In this course, you'll first explore predictive AI versus generative AI and see how each poses distinct risks and ethical considerations. We'll compare weak AI versus strong AI. You'll also delve into the major model families (regression, tree based methods, neural networks, and more) and learn how to evaluate their performance using accuracy metrics such as precision, recall, or mean squared error.From an ethics perspective, we'll show why fair and transparent AI design is vital. Through case studies and best practices, you'll discover how to mitigate bias, protect user privacy, and address accountability. We'll also unpack governance frameworks from Singapore, the World Economic Forum, the Organization for Economic Cooperation and Development, European Union and others complete with practical questionnaires that spotlight potential pitfalls in AI deployment. By the end, you'll have the knowledge and tools to ensure your AI projects uphold public trust and safeguard the well being of everyone they affect. Best of all, this course is led by an industry veteran who brings a wealth of corporate and technical experience especially around managing AI deployments in real world settings. Through his first hand insights, you'll gain practical guidance on navigating common pitfalls, championing ethical decision making, and ensuring that AI solutions deliver true, sustainable impact.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Understand AI
Lecture 2 Strong AI vs Weak AI
Lecture 3 Gen-AI and Predictive AI
Lecture 4 Types of Algorithms
Lecture 5 How algorithms work
Lecture 6 How neural networks operate
Lecture 7 Different types of algorithms
Lecture 8 Accuracy of algorithms
Lecture 9 AI Lifecycle
Section 3: Explainable AI (XAI)
Lecture 10 Introduction to XAI
Lecture 11 Types of AI Models from Explainability Perspective
Lecture 12 Types of Explainability Techniques
Section 4: AI Ethics and Governance: Setting the context
Lecture 13 Need for AI Ethics and Governance
Lecture 14 Building Blocks of AI Ethics
Lecture 15 Metrics for AI Ethics
Section 5: AI Ethics and Governance Framework: Singapore
Lecture 16 Introduction to Singapore Framework
Lecture 17 Questionaire
Lecture 18 Veritas Tooklit
Lecture 19 FEAT principles
Section 6: World Economic Forum (WEF) : AI Toolkit & Model Governance
Lecture 20 Introduction
Lecture 21 Implementing WEF Governance
Lecture 22 WEF Best Practices
Section 7: European Commission's Ethics Guidelines for Trustworthy AI
Lecture 23 Introduction to Trustworthy AI
Lecture 24 Questionnaire for Trustworthy AI
Section 8: IEEE's Ethically Aligned Design
Lecture 25 Introduction to IEEE's Ethically Aligned Design
Lecture 26 IEEE Key Chapters
Lecture 27 P7001
Lecture 28 P7002
Lecture 29 P7003
Section 9: Other AI Ethics and Governance Frameworks
Lecture 30 OECD
CxO's,AI Ethics and Governance Enthusiasts,Data Scientists,Machine Learning Engineers,IT Professionals,Students
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