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    Ai Governance Professional (Aigp) Certification & Ai Mastery

    Posted By: ELK1nG
    Ai Governance Professional (Aigp) Certification & Ai Mastery

    Ai Governance Professional (Aigp) Certification & Ai Mastery
    Published 9/2024
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 5.37 GB | Duration: 27h 27m

    Master the 7 Domains of the AIGP Certification with Expert Guidance in AI Governance and Ethical Standards

    What you'll learn

    The distinction between narrow and general AI and how these systems operate within various industries.

    Core principles of machine learning including supervised, unsupervised, and reinforcement learning techniques.

    Advanced AI concepts such as deep learning and transformer models, with a focus on their theoretical foundations.

    Natural Language Processing (NLP) and multi-modal models, and their application in enhancing AI systems.

    The ethical and societal implications of AI, including its impact on privacy, discrimination, and public trust.

    Global AI governance frameworks, including standards from the OECD, EU, and other international bodies.

    Responsible AI principles, focusing on transparency, accountability, and human-centric design in AI systems.

    The legal and regulatory landscape for AI, covering laws related to non-discrimination, data protection, and intellectual property.

    AI development life cycle, from defining business objectives and governance structures to model testing and validation.

    Post-deployment AI system management, including monitoring, validation, and addressing automation bias.

    Requirements

    No Prerequisites.

    Description

    This course is designed to provide a deep theoretical understanding of the fundamental concepts that underpin AI and machine learning (ML) technologies, with a specific focus on preparing students for the AI Governance Professional (AIGP) Certification. Throughout the course, students will explore the 7 critical domains required for certification: AI governance and risk management, regulatory compliance, ethical AI frameworks, data privacy and protection, AI bias mitigation, human-centered AI, and responsible AI innovation. Mastery of these domains is essential for navigating the ethical, legal, and governance challenges posed by AI technologies.Students will explore key ideas driving AI innovation, with a particular focus on understanding the various types of AI systems, including narrow and general AI. This distinction is crucial for understanding the scope and limitations of current AI technologies, as well as their potential future developments. The course also delves into machine learning basics, explaining different training methods and algorithms that form the core of intelligent systems.As AI continues to evolve, deep learning and transformer models have become integral to advancements in the field. Students will examine these theoretical frameworks, focusing on their roles in modern AI applications, particularly in generative AI and natural language processing (NLP). Additionally, the course addresses multi-modal models, which combine various data types to enhance AI capabilities in fields such as healthcare and education. The interdisciplinary nature of AI will also be discussed, highlighting the collaboration required between technical experts and social scientists to ensure responsible AI development.The history and evolution of AI are critical to understanding the trajectory of these technologies. The course will trace AI’s development from its early stages to its current status as a transformative tool in many industries. This historical context helps frame the ethical and social responsibilities associated with AI. A key component of the course involves discussing AI’s broader impacts on society, from individual harms such as privacy violations to group-level biases and discrimination. Students will gain insight into how AI affects democratic processes, education, and public trust, as well as the potential economic repercussions, including the redistribution of jobs and economic opportunities.In exploring responsible AI, the course emphasizes the importance of developing trustworthy AI systems. Students will learn about the core principles of responsible AI, such as transparency, accountability, and human-centric design, which are essential for building ethical AI technologies. The course also covers privacy-enhanced AI systems, discussing the balance between data utility and privacy protection. To ensure students understand the global regulatory landscape, the course includes an overview of international standards for trustworthy AI, including frameworks established by organizations like the OECD and the EU.A key aspect of this course is its comprehensive preparation for the AI Governance Professional (AIGP) Certification. This certification focuses on equipping professionals with the knowledge and skills to navigate the ethical, legal, and governance challenges posed by AI technologies. The AIGP Certification provides significant benefits, including enhanced credibility in AI ethics and governance, a deep understanding of global AI regulatory frameworks, and the ability to effectively manage AI risks in various industries. By earning this certification, students will be better positioned to lead organizations in implementing responsible AI practices and ensuring compliance with evolving regulations.Another critical aspect of the course is understanding the legal and regulatory frameworks that govern AI development and deployment. Students will explore AI-specific laws and regulations, including non-discrimination laws and privacy protections that apply to AI applications. This section of the course will provide an in-depth examination of key legislative efforts worldwide, including the EU Digital Services Act and the AI-related provisions of the GDPR. By understanding these frameworks, students will gain insight into the legal considerations that must be navigated when deploying AI systems.Finally, the course will walk students through the AI development life cycle, focusing on the theoretical aspects of planning, governance, and risk management. Students will learn how to define business objectives for AI projects, establish governance structures, and address challenges related to data strategy and model selection. Ethical considerations in AI system architecture will also be explored, emphasizing the importance of fairness, transparency, and accountability. The course concludes by discussing the post-deployment management of AI systems, including monitoring, validation, and ensuring ethical operation throughout the system's life cycle.Overall, this course offers a comprehensive theoretical foundation in AI and machine learning, focusing on the ethical, social, and legal considerations necessary for the responsible development and deployment of AI technologies. It provides students not only with a strong understanding of AI governance and societal impacts but also prepares them to obtain the highly regarded AI Governance Professional (AIGP) Certification, enhancing their career prospects in the rapidly evolving field of AI governance.

    Overview

    Section 1: Course Resources and Downloads

    Lecture 1 Course Resources and Downloads

    Section 2: Foundations of AI and Machine Learning

    Lecture 2 Section Introduction

    Lecture 3 Introduction to AI and Machine Learning

    Lecture 4 Case Study: AI-Diagnosis: Transforming Healthcare with AI and ML

    Lecture 5 Types of AI Systems: Narrow vs. General AI

    Lecture 6 Case Study: Navigating AI Governance

    Lecture 7 Machine Learning Basics and Training Methods

    Lecture 8 Case Study: Enhancing Customer Churn Prediction

    Lecture 9 Deep Learning, Generative AI, and Transformer Models

    Lecture 10 Case Study: Transformative AI: Integrating Deep Learning

    Lecture 11 Natural Language Processing and Multi-modal Models

    Lecture 12 Case Study: Revolutionizing Healthcare and Education with NLP and Multi-Modal AI

    Lecture 13 Socio-technical AI Systems and Cross-disciplinary Collaboration

    Lecture 14 Case Study: Integrating Technical Excellence and Social Responsibility

    Lecture 15 The History and Evolution of AI and Data Science

    Lecture 16 Case Study: Bridging AI's Past and Present

    Lecture 17 Section Summary

    Section 3: Understanding AI Impacts on Society

    Lecture 18 Section Introduction

    Lecture 19 Individual Harms: Civil Rights, Safety, and Economic Impact

    Lecture 20 Case Study: Navigating AI's Challenges

    Lecture 21 Group Harms: Discrimination and Bias in AI Systems

    Lecture 22 Case Study: Addressing AI Bias

    Lecture 23 Societal Harms: Democracy, Education, and Public Trust

    Lecture 24 Case Study: AI's Impact on Democracy, Education, and Public Trust

    Lecture 25 Organizational Risks: Reputational, Cultural, and Economic Threats

    Lecture 26 Case Study: Navigating AI Governance

    Lecture 27 Environmental and Ecosystem Impacts of AI

    Lecture 28 Case Study: Balancing AI Progress with Sustainability

    Lecture 29 Redistribution of Jobs and Economic Opportunities Due to AI

    Lecture 30 Case Study: Balancing AI Integration and Workforce Reskilling

    Lecture 31 AI's Impact on Workforce and Educational Access

    Lecture 32 Case Study: TechNova's Strategic Approach to Workforce Reskilling

    Lecture 33 Section Summary

    Section 4: Responsible AI Principles and Trustworthy AI

    Lecture 34 Section Introduction

    Lecture 35 Core Principles of Responsible AI

    Lecture 36 Case Study: Building Ethical AI

    Lecture 37 Human-centric AI Systems

    Lecture 38 Case Study: Human-Centric AI for Urban Traffic Management

    Lecture 39 Transparency, Explainability, and Accountability in AI

    Lecture 40 Case Study: Balancing Innovation and Ethics

    Lecture 41 Safe, Secure, and Resilient AI Systems

    Lecture 42 Case Study: Ensuring Ethical, Secure, and Resilient AI

    Lecture 43 Privacy-Enhanced AI Systems and Data Protection

    Lecture 44 Case Study: Balancing Data Utility and Privacy in AI

    Lecture 45 OECD and EU Standards for Trustworthy AI

    Lecture 46 Case Study: Navigating Ethical Challenges in AI-Driven Healthcare Innovation

    Lecture 47 Comparison of Global Ethical Guidelines for AI

    Lecture 48 Case Study: Navigating Global Ethical Standards for AI

    Lecture 49 Section Summary

    Section 5: AI Laws and Regulatory Compliance

    Lecture 50 Section Introduction

    Lecture 51 Overview of AI-Specific Laws and Regulations

    Lecture 52 Case Study: Navigating Global AI Regulations

    Lecture 53 Non-Discrimination Laws and AI Applications

    Lecture 54 Case Study: Mitigating AI Bias: DiversiHire's Journey Through Fairness

    Lecture 55 Product Safety Laws for AI Systems

    Lecture 56 Case Study: Ensuring AI Safety

    Lecture 57 Privacy and Data Protection in AI Systems

    Lecture 58 Case Study: Balancing AI Innovation with Privacy and Ethics

    Lecture 59 Intellectual Property and AI: Legal Considerations

    Lecture 60 Case Study: Navigating AI and IP Law

    Lecture 61 Key Components of the EU Digital Services Act

    Lecture 62 Case Study: Navigating DSA Compliance

    Lecture 63 The Intersection of AI and GDPR Requirements

    Lecture 64 Case Study: Balancing AI Innovation and GDPR Compliance

    Lecture 65 Section Summary

    Section 6: Global AI Legal Frameworks

    Lecture 66 Section Introduction

    Lecture 67 Overview of the EU AI Act and Its Risk Categories

    Lecture 68 Case Study: Implementing the EU AI Act

    Lecture 69 Requirements for High-Risk AI Systems and Foundation Models

    Lecture 70 Case Study: Ensuring Ethical and Effective Deployment of High-Risk AI

    Lecture 71 Notification and Enforcement Mechanisms under the EU AI Act

    Lecture 72 Case Study: TechNova's Strategic Response to EU AI Act Compliance Challenges

    Lecture 73 Canada's Artificial Intelligence and Data Act (Bill C-27)

    Lecture 74 Case Study: Balancing AI Innovation and Ethical Governance

    Lecture 75 Key Components of U.S. AI-related State Laws

    Lecture 76 Case Study: Navigating AI Regulations

    Lecture 77 China's Draft Regulations on Generative AI

    Lecture 78 Case Study: Navigating China's AI Regulations

    Lecture 79 Harmonizing Global AI Laws and Risk Management Frameworks

    Lecture 80 Case Study: Harmonizing Global AI Laws

    Lecture 81 Section Summary

    Section 7: AI Development Life Cycle - Planning

    Lecture 82 Section Introduction

    Lecture 83 Defining Business Objectives and AI System Scope

    Lecture 84 Case Study: Optimizing Customer Service with AI

    Lecture 85 Determining AI Governance Structures and Responsibilities

    Lecture 86 Case Study: Ethical AI Governance

    Lecture 87 Data Strategy: Collection, Labeling, and Cleaning

    Lecture 88 Case Study: TechNova's AI Chatbot Success

    Lecture 89 Model Selection: Accuracy vs. Interpretability

    Lecture 90 Case Study: Balancing Accuracy and Interpretability in AI

    Lecture 91 Ethical Design in AI System Architecture

    Lecture 92 Case Study: FairAI's Commitment to Fairness, Transparency, and Accountability

    Lecture 93 Understanding the Governance Challenges in AI Planning

    Lecture 94 Case Study: Governance Challenges in AI Planning

    Lecture 95 Cross-functional Team Collaboration in AI Planning

    Lecture 96 Case Study: Cross-Functional Synergy

    Lecture 97 Section Summary

    Section 8: AI Development Life Cycle - Development and Testing

    Lecture 98 Section Introduction

    Lecture 99 Feature Engineering for AI Models

    Lecture 100 Case Study: Enhancing Predictive Health Analytics

    Lecture 101 Model Training: Techniques and Best Practices

    Lecture 102 Case Study: Optimizing AI for Rare Disease Detection

    Lecture 103 Model Testing and Validation Processes

    Lecture 104 Case Study: Rigorous Testing and Ethical Considerations

    Lecture 105 Testing AI Models with Edge Cases and Adversarial Inputs

    Lecture 106 Case Study: Ensuring Robustness and Reliability in Autonomous Drone AI

    Lecture 107 Privacy-preserving Machine Learning Techniques

    Lecture 108 Case Study: Balancing Privacy and Utility

    Lecture 109 Repeatability Assessments and Model Fact Sheets

    Lecture 110 Case Study: Ensuring AI Model Reliability and Transparency

    Lecture 111 Conducting Algorithm Impact Assessments

    Lecture 112 Case Study: Ensuring Fairness and Accountability

    Lecture 113 Section Summary

    Section 9: Implementing AI Governance and Risk Management

    Lecture 114 Section Introduction

    Lecture 115 Creating AI Risk Management Frameworks

    Lecture 116 Case Study: Comprehensive AI Risk Management

    Lecture 117 AI Governance Infrastructure: Key Roles and Responsibilities

    Lecture 118 Case Study: Comprehensive AI Governance

    Lecture 119 Cross-functional Collaboration in AI Governance

    Lecture 120 Case Study: Cross-Functional Collaboration

    Lecture 121 AI Regulatory Requirements and Compliance Procedures

    Lecture 122 Case Study: TechNova's Path to Ethical and Compliant AI

    Lecture 123 Establishing a Responsible AI Culture within Organizations

    Lecture 124 Case Study: Establishing Responsible AI

    Lecture 125 Assessing AI Maturity Levels in Business Functions

    Lecture 126 Case Study: Enhancing AI Maturity

    Lecture 127 Managing Third-Party Risks in AI Systems

    Lecture 128 Case Study: Managing Third-Party Risks in AI

    Lecture 129 Section Summary

    Section 10: AI Project Management and Risk Analysis

    Lecture 130 Section Introduction

    Lecture 131 Scoping AI Projects: Identifying Key Objectives

    Lecture 132 Case Study: Strategic Scoping of AI Projects

    Lecture 133 Mapping AI Risks: Identifying Internal and External Threats

    Lecture 134 Case Study: Overcoming Challenges in Developing an AI-Driven Recruitment Tool

    Lecture 135 Developing Risk Mitigation Strategies for AI Projects

    Lecture 136 Case Study: Comprehensive Risk Management Strategies for Successful AI Projects

    Lecture 137 Constructing a Harms Matrix for AI Risk Assessment

    Lecture 138 Case Study: Harms Matrix: Mitigating Risks in AI-Driven Cancer Diagnostics

    Lecture 139 Conducting Algorithm Impact Assessments

    Lecture 140 Case Study: TechNova's AI Hiring Algorithm

    Lecture 141 Engaging Stakeholders in AI Risk Management

    Lecture 142 Case Study: Ensuring Ethical AI

    Lecture 143 Data Provenance, Lineage, and Accuracy in AI Systems

    Lecture 144 Case Study: Ensuring Data Integrity and Transparency in AI Systems

    Lecture 145 Section Summary

    Section 11: Post-Deployment AI System Management

    Lecture 146 Section Introduction

    Lecture 147 Continuous Monitoring and Validation of AI Systems

    Lecture 148 Case Study: Continuous Monitoring and Ethical Oversight

    Lecture 149 Post-Hoc Testing for AI System Accuracy and Effectiveness

    Lecture 150 Case Study: Ensuring AI Tool Accuracy, Fairness, and Robustness

    Lecture 151 Managing Automation Bias in AI Systems

    Lecture 152 Case Study: Balancing AI and Clinical Judgment

    Lecture 153 Model Versioning and Updates: Best Practices

    Lecture 154 Case Study: Structured AI Model Versioning

    Lecture 155 Managing Third-Party Risks Post-Deployment

    Lecture 156 Case Study: Managing Third-Party Risks

    Lecture 157 Reducing Unintended Use and Downstream Harm in AI Systems

    Lecture 158 Case Study: Ethical Governance and Transparency in AI-Driven Healthcare

    Lecture 159 Planning for AI System Deactivation and System Sunset

    Lecture 160 Case Study: Effective Strategies for AI System Deactivation

    Lecture 161 Section Summary

    Section 12: AI Ethics and Accountability

    Lecture 162 Section Introduction

    Lecture 163 Building a Global AI Auditing Framework

    Lecture 164 Case Study: Global AI Auditing Framework

    Lecture 165 Establishing AI Auditing Standards and Compliance Measures

    Lecture 166 Case Study: Implementing Ethical AI Auditing

    Lecture 167 Accountability in Automated Decision-Making Systems

    Lecture 168 Case Study: Ensuring Accountability and Fairness in AI-Driven Loan Approval

    Lecture 169 Enhancing AI Governance with Automated Compliance Tools

    Lecture 170 Case Study: Enhancing AI Governance

    Lecture 171 Ethical Dilemmas in AI Governance and Deployment

    Lecture 172 Case Study: Navigating Ethical Challenges in AI Deployment

    Lecture 173 Understanding AI Failures: Bias, Hallucinations, and Errors

    Lecture 174 Case Study: Mitigating AI Bias, Hallucinations, and Errors

    Lecture 175 Managing Cultural and Behavioral Change in AI Teams

    Lecture 176 Case Study: TechNova's Journey in Managing Cultural and Behavioral Change

    Lecture 177 Section Summary

    Section 13: Emerging AI Technologies and Future Trends

    Lecture 178 Section Introduction

    Lecture 179 Advances in Generative AI and Multi-modal AI Models

    Lecture 180 Case Study: Revolutionizing Healthcare with Generative and Multi-Modal AI

    Lecture 181 Natural Language Processing (NLP) and Large Language Models

    Lecture 182 Case Study: Revolutionizing Customer Support with NLP and LLMs

    Lecture 183 AI in Robotics, Automation, and Autonomous Systems

    Lecture 184 Case Study: AI-Driven Innovations

    Lecture 185 AI's Role in the Metaverse, AR, and VR

    Lecture 186 Case Study: Integrating AI in the Metaverse

    Lecture 187 Emerging Trends in AI for Healthcare and Medicine

    Lecture 188 Case Study: AI Revolutionizing Healthcare

    Lecture 189 AI in Environmental and Sustainability Applications

    Lecture 190 Case Study: AI-Powered Sustainability

    Lecture 191 Predicting the Future of AI: Trends and Challenges

    Lecture 192 Case Study: AI in Healthcare: Balancing Innovation, Ethics, and Governance

    Lecture 193 Section Summary

    Section 14: AI in the Socio-Cultural Context

    Lecture 194 Section Introduction

    Lecture 195 AI's Impact on Jobs and Employment Opportunities

    Lecture 196 Case Study: Transforming Employment

    Lecture 197 The Redistribution of Wealth and Economic Power via AI

    Lecture 198 Case Study: Navigating Inequality, Market Shifts, and Regulatory Challenges

    Lecture 199 AI's Influence on Education and Lifelong Learning

    Lecture 200 Case Study: Personalized Learning, Efficiency, and Inclusivity at Westbrook High

    Lecture 201 Public Trust in AI and Its Governance

    Lecture 202 Case Study: The HealthAI Case Study on Governance and Ethical Integration

    Lecture 203 AI and Democratic Processes: Challenges and Opportunities

    Lecture 204 Case Study: AI's Impact on Democracy

    Lecture 205 Building Inclusive AI Systems for Diverse Societies

    Lecture 206 Case Study: TechNova's Journey to Equitable Job Recruitment Systems

    Lecture 207 Case Study: Strategic Innovation and Adaptability

    Lecture 208 Section Summary

    Section 15: AI Auditing, Evaluation, and Impact Measurement

    Lecture 209 Section Introduction

    Lecture 210 Methods and Tools for Conducting AI Audits

    Lecture 211 Case Study: Comprehensive AI Audit at TechNova

    Lecture 212 Evaluating AI's Societal Impact: Metrics and Approaches

    Lecture 213 Case Study: Evaluating AI's Societal Impact

    Lecture 214 Tracking AI System Performance Post-Deployment

    Lecture 215 Case Study: Optimizing AI Post-Deployment

    Lecture 216 Remediating AI System Failures and Negative Impacts

    Lecture 217 Case Study: Enhancing AI Governance

    Lecture 218 Reporting and Communicating AI System Risks

    Lecture 219 Case Study: Ensuring AI Integrity

    Lecture 220 Creating Ethical AI Impact Reports for Stakeholders

    Lecture 221 Case Study: Transparency, Fairness, Privacy, Accountability, and Societal Impact

    Lecture 222 Preparing AI Systems for Continuous Evaluation and Updates

    Lecture 223 Case Study: Continuous Improvement and Reliability

    Lecture 224 Section Summary

    Section 16: Contemplating Ongoing AI Issues and Challenges

    Lecture 225 Section Introduction

    Lecture 226 Legal Challenges of AI: Tort Liability and Responsibility

    Lecture 227 Case Study: AI Liability in Autonomous Vehicle Accidents

    Lecture 228 Intellectual Property Rights and AI System Ownership

    Lecture 229 Case Study: AI-Generated Art and Intellectual Property

    Lecture 230 Educating Users on the Functions and Limitations of AI

    Lecture 231 Case Study: Harnessing AI Responsibly

    Lecture 232 Addressing Workforce Upskilling and Reskilling Needs

    Lecture 233 Case Study: Navigating AI-Driven Workforce Transformation

    Lecture 234 Building a Profession of AI Auditors: Standards and Training

    Lecture 235 Case Study: Ensuring Ethical and Fair AI

    Lecture 236 Automated Governance for AI Ethical Issues

    Lecture 237 Case Study: Ethical AI Governance

    Lecture 238 Preparing for the Future of AI Governance and Ethics

    Lecture 239 Case Study: Navigating Ethical AI Governance

    Lecture 240 Section Summary

    Section 17: Course Summary

    Lecture 241 Conclusion

    Aspiring AI leaders seeking comprehensive knowledge in AI governance,AI professionals aiming to enhance their expertise in ethical AI practices,Policy makers interested in understanding AI regulatory landscapes,Risk management experts focusing on AI-related challenges and solutions,Corporate strategists looking to implement effective AI governance measures,Academics and researchers exploring the ethical and societal impacts of AI,Public sector employees involved in AI policy development and implementation,Individuals committed to responsible and equitable AI governance practices