Certified AI Ethics & Governance Professional (CAEGP)

Free Download Certified AI Ethics & Governance Professional (CAEGP)
Published 11/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.27 GB | Duration: 18h 14m
Build a Strong Foundation in Ethical AI Principles and Governance Strategies for Responsible Innovation

What you’ll learn
Key concepts and terminology of AI ethics and governance.
Importance and principles of responsible AI practices.
Basics of AI and machine learning in a business context.
Ethical challenges associated with AI development.
Fairness and non-discrimination in AI systems.
Accountability and transparency in AI model design.
Privacy protection and data security in AI applications.
Techniques to identify and reduce bias in AI systems.
Strategies for risk assessment and mitigation in AI.
Building effective AI governance structures in organizations.
Understanding global AI regulations and compliance.
Aligning AI practices with ISO and IEEE standards.
Implementing privacy-by-design principles in AI.
Developing ethical AI policies and governance frameworks.
Responsible AI decision-making for customer interactions.
Preparing for future ethical challenges in AI innovation.
Requirements
No Prerequisites.
Description
In an era where technology is advancing at an unprecedented rate, the ethical considerations surrounding artificial intelligence (AI) have become a vital concern. This course aims to equip students with a comprehensive understanding of the theoretical foundations necessary to navigate the complex landscape of AI ethics and governance. The course begins with an introduction to the essential concepts and terminology, ensuring that participants have a solid grounding in what ethical AI entails. Early lessons establish the significance of responsible AI practices and underscore why adherence to ethical standards is critical for developers, businesses, and policymakers.Students will gain insights into the core principles underlying AI and machine learning, providing the context needed to appreciate how these technologies intersect with society at large. Discussions include the fundamental workings of key AI technologies and their broad applications across industries, emphasizing the societal impact of these systems. The potential risks associated with AI, such as bias, data privacy issues, and transparency challenges, are highlighted to illustrate the importance of proactive ethical frameworks. By exploring these foundational topics, students can better understand the intricate balance between innovation and ethical responsibility.The course delves into the fundamental ethical principles that must guide AI development, focusing on fairness, accountability, transparency, and privacy. Lessons are designed to present theoretical approaches to avoiding bias in AI systems and fostering equitable outcomes. The emphasis on explainability ensures that students recognize the significance of creating models that can be interpreted and trusted by a range of stakeholders, from developers to end-users. Moreover, privacy and data protection in AI are examined, stressing the importance of embedding these values into the design phase of AI systems.An essential part of ethical AI development is risk management, which this course explores in depth. Lessons outline how to identify and assess potential AI risks, followed by strategies for managing and mitigating these challenges effectively. Students will learn about various risk management frameworks and the importance of planning for contingencies to address potential failures in AI systems. This theoretical approach prepares students to anticipate and counteract the ethical dilemmas that may arise during the AI lifecycle.Governance plays a crucial role in shaping responsible AI practices. The course introduces students to the structures and policies essential for effective AI governance. Lessons on developing and implementing governance frameworks guide students on how to align AI practices with organizational and regulatory requirements. Emphasizing the establishment of accountability mechanisms within governance structures helps highlight the responsibilities that organizations bear when deploying AI systems.A segment on the regulatory landscape provides students with an overview of global AI regulations, including GDPR and the California Consumer Privacy Act (CCPA), among others. These lessons emphasize the need for compliance with data privacy laws and other legislative measures, ensuring students are aware of how regulation shapes the ethical deployment of AI. By understanding the regulatory backdrop, students can appreciate the intersection of policy and practice in maintaining ethical standards.The course also covers standards and guidelines established by leading industry organizations, such as ISO and IEEE. These lessons are crafted to present emerging best practices and evolving standards that guide ethical AI integration. Understanding these standards allows students to grasp the nuances of aligning technology development with recognized ethical benchmarks.Data privacy is a pillar of ethical AI, and this course offers lessons on the importance of securing data throughout AI processes. Topics include strategies for data anonymization and minimization, as well as approaches to handling sensitive data. By integrating theoretical knowledge on how to ensure data security in AI systems, students will be well-equipped to propose solutions that prioritize user privacy without compromising innovation.The final sections of the course concentrate on the ethical application of AI in business. Lessons illustrate how to apply AI responsibly in decision-making and customer interaction, ensuring that technology acts as a force for good. Theoretical explorations of AI for social sustainability emphasize the broader societal responsibilities of leveraging AI, fostering a mindset that goes beyond profit to consider ethical impacts.Throughout the course, the challenges of bias and fairness in AI are explored, including techniques for identifying and reducing bias. Discussions on the legal implications of bias underscore the consequences of failing to implement fair AI systems. Additionally, students will learn about creating transparency and accountability in AI documentation, further solidifying their ability to champion responsible AI practices.This course provides an in-depth exploration of AI ethics and governance through a theoretical lens, focusing on fostering a robust understanding of responsible practices and governance strategies essential for the ethical development and deployment of AI systems.
Overview
Section 1: Course Resources and Downloads
Lecture 1 Course Resources and Downloads
Section 2: Introduction to AI Ethics and Governance
Lecture 2 Section Introduction
Lecture 3 Overview of AI Ethics and Governance
Lecture 4 Case Study: AI Nexus’s Journey to Fair, Transparent, and Private Solutions
Lecture 5 Importance of Responsible AI Practices
Lecture 6 Case Study: Case Study: Navigating Ethical AI: InnovateAI’s Journey
Lecture 7 Key Concepts and Terminology
Lecture 8 Case Study: TechNova’s Journey in Fairness, Transparency, and Accountability
Lecture 9 Introduction to CAEGP Certification Domains
Lecture 10 Case Study: Navigating AI Ethics and Governance
Lecture 11 Ethical Challenges in AI
Lecture 12 Case Study: Ethical AI in Healthcare: Addressing Bias, Privacy, and Transparency
Lecture 13 Section Summary
Section 3: Foundations of AI and Machine Learning
Lecture 14 Section Introduction
Lecture 15 Basics of AI and Machine Learning
Lecture 16 Case Study: Integrating Innovation, Ethics, and Data-Driven Strategies
Lecture 17 Key AI Technologies and Applications
Lecture 18 Case Study: GreenTech’s Path to Ethical and Innovative Energy Solutions
Lecture 19 Understanding AI in a Business Context
Lecture 20 Case Study: Integrating AI at TechNova: A Strategic Innovation Journey
Lecture 21 AI and Its Impact on Society
Lecture 22 Case Study: Balancing Innovation and Ethics
Lecture 23 Risks Associated with AI Development
Lecture 24 Case Study: Navigating AI Ethics and Risks
Lecture 25 Section Summary
Section 4: Ethical Principles in AI
Lecture 26 Section Introduction
Lecture 27 Fairness and Non-Discrimination
Lecture 28 Case Study: Ensuring Fairness and Mitigating Bias in AI for TechNova’s
Lecture 29 Accountability in AI
Lecture 30 Case Study: Enhancing AI Accountability: InnovateAI’s Journey
Lecture 31 Transparency and Explainability
Lecture 32 Case Study: Transparency, Explainability, and Ethical Compliance
Lecture 33 Privacy and Data Protection in AI
Lecture 34 Case Study: Balancing Innovation and Privacy
Lecture 35 Avoiding Bias in AI Systems
Lecture 36 Case Study: Mitigating AI Bias: A Case Study on Ethical Challenges
Lecture 37 Section Summary
Section 5: Responsible AI Design
Lecture 38 Section Introduction
Lecture 39 AI Design for Ethical Outcomes
Lecture 40 Case Study: AvaTech’s Approach to Bias and Stakeholder Engagement
Lecture 41 Privacy by Design in AI
Lecture 42 Case Study: Integrating Privacy by Design
Lecture 43 Explainability and Interpretability in AI Models
Lecture 44 Case Study: Balancing Explainability and Accuracy
Lecture 45 Impact Assessment in AI Design
Lecture 46 Case Study: HealthTech’s Journey to Responsible Innovation
Lecture 47 Human-Centered AI Design
Lecture 48 Case Study: Designing Ethical and Inclusive AI
Lecture 49 Section Summary
Section 6: Risk Management in AI
Lecture 50 Section Introduction
Lecture 51 Identifying and Assessing AI Risks
Lecture 52 Case Study: TechNova’s Strategic Approach to Safe and Effective Implementation
Lecture 53 Risk Management Frameworks
Lecture 54 Case Study: TechNova’s Strategy for Ethical Deployment
Lecture 55 Mitigating Risks in AI Systems
Lecture 56 Case Study: Strategies for Safe and Ethical Deployment in Healthcare
Lecture 57 Contingency Planning for AI Failures
Lecture 58 Case Study: Lessons from TechNova’s Contingency Planning Case Study
Lecture 59 Monitoring AI Systems for Risk
Lecture 60 Case Study: DataSecure Inc.’s Approach to Risk and Governance
Lecture 61 Section Summary
Section 7: AI Governance Frameworks
Lecture 62 Section Introduction
Lecture 63 Introduction to Governance in AI
Lecture 64 Case Study: Navigating Ethical Challenges in Traffic Management
Lecture 65 Key Components of AI Governance
Lecture 66 Case Study: Ethical AI Solutions in Healthcare Management
Lecture 67 Developing an AI Governance Framework
Lecture 68 Case Study: Crafting an Ethical AI Governance Framework
Lecture 69 Implementing Governance Policies in AI
Lecture 70 Case Study: Balancing Ethics, Transparency, Accountability, and Privacy
Lecture 71 Establishing Governance Accountability
Lecture 72 Case Study: Ensuring Ethical AI: InnovoTech’s Journey in Governance
Lecture 73 Section Summary
Section 8: Regulatory Landscape for AI
Lecture 74 Section Introduction
Lecture 75 Overview of AI Regulations Worldwide
Lecture 76 Case Study: Balancing Global AI Innovation and Compliance Across Borders
Lecture 77 GDPR and Data Privacy in AI
Lecture 78 Case Study: Balancing GDPR Compliance and AI Innovation
Lecture 79 California Consumer Privacy Act (CCPA)
Lecture 80 Case Study: DataGuard’s Strategy for Privacy and Innovation Balance
Lecture 81 AI Regulations in Asia and Europe
Lecture 82 Case Study: Navigating AI Regulation
Lecture 83 Adapting to Evolving AI Regulations
Lecture 84 Case Study: TechNova’s Strategy for Compliance and Ethical Innovation
Lecture 85 Section Summary
Section 9: Standards and Guidelines for Ethical AI
Lecture 86 Section Introduction
Lecture 87 ISO and IEEE Standards for AI
Lecture 88 Case Study: Navigating Ethical AI Integration in Healthcare
Lecture 89 AI Ethics and Governance Guidelines
Lecture 90 Case Study: Addressing Bias, Accountability, and Privacy in Recruitment
Lecture 91 Best Practices from Industry Leaders
Lecture 92 Case Study: TechNova’s Ethical AI: Enhancing Governance, Transparency, and Trust
Lecture 93 Emerging Standards in AI Ethics
Lecture 94 Case Study: Integrating Ethical AI
Lecture 95 Compliance with AI Standards
Lecture 96 Case Study: Navigating Ethical Compliance in AI
Lecture 97 Section Summary
Section 10: Data Privacy and Protection in AI
Lecture 98 Section Introduction
Lecture 99 Importance of Data Privacy in AI
Lecture 100 Case Study: Balancing AI Innovation and Data Privacy
Lecture 101 Compliance with Data Protection Laws
Lecture 102 Case Study: Ensuring AI Ethics and Compliance in Data Protection
Lecture 103 Data Anonymization and Minimization
Lecture 104 Case Study: DataGuard’s Ethical AI Journey in Health Data Protection
Lecture 105 Handling Sensitive Data in AI Systems
Lecture 106 Case Study: DataGuard Inc.’s Approach to Secure AI in Healthcare
Lecture 107 Ensuring Data Security in AI
Lecture 108 Case Study: Navigating AI Privacy and Security Challenges with GDPR Compliance
Lecture 109 Section Summary
Section 11: Ethical Use of AI in Business
Lecture 110 Section Introduction
Lecture 111 Ethical AI Applications in Industry
Lecture 112 Case Study: Navigating Ethical Challenges in AI
Lecture 113 AI in Decision-Making: Ethical Considerations
Lecture 114 Case Study: Ethical Challenges and Strategies in AI-Driven Credit Scoring
Lecture 115 The Role of AI in Customer Interaction
Lecture 116 Case Study: Leveraging AI for Enhanced Customer Interaction
Lecture 117 AI for Social Good and Sustainability
Lecture 118 Case Study: AI-Driven Sustainability
Lecture 119 Responsible AI Use Cases
Lecture 120 Case Study: TechNova’s Strategy for Responsible Innovation
Lecture 121 Section Summary
Section 12: Addressing Bias and Fairness in AI
Lecture 122 Section Introduction
Lecture 123 Understanding Bias in AI Models
Lecture 124 Case Study: TechNova’s Journey to Mitigate Bias in Recruitment Models
Lecture 125 Techniques to Identify Bias
Lecture 126 Case Study: InnovateAI’s Journey to Ethical Facial Recognition
Lecture 127 Methods for Reducing Bias in AI
Lecture 128 Case Study: Optima Technologies’ Path to Fair and Inclusive Solutions
Lecture 129 Ensuring Fairness in AI Outcomes
Lecture 130 Case Study: MediTech’s Approach to Bias Mitigation in Healthcare AI Systems
Lecture 131 Legal Implications of AI Bias
Lecture 132 Case Study: TechNova’s Strategy for Fair and Transparent Recruitment Systems
Lecture 133 Section Summary
Section 13: Accountability and Transparency in AI
Lecture 134 Section Introduction
Lecture 135 Building Accountability into AI Systems
Lecture 136 Case Study: Embedding Accountability in AI
Lecture 137 Importance of Transparency in AI
Lecture 138 Case Study: MedAI’s Ethical and Operational Challenges in Healthcare
Lecture 139 Documentation and Disclosure Practices
Lecture 140 Case Study: Accountability and Transparency in Healthcare and Finance
Lecture 141 Transparency Reporting in AI
Lecture 142 Case Study: Enhancing AI Transparency: Lessons from the COMPAS Case Study
Lecture 143 Balancing Transparency with Security
Lecture 144 Case Study: Balancing AI Transparency and Security in Healthcare and Finance
Lecture 145 Section Summary
Section 14: AI Governance and Compliance in Organizations
Lecture 146 Section Introduction
Lecture 147 Organizational Structures for AI Governance
Lecture 148 Case Study: Balancing Innovation and Ethical Accountability
Lecture 149 Role of Compliance Officers in AI Governance
Lecture 150 Case Study: Compliance Officers as Ethical AI Enablers at FinServe
Lecture 151 Integrating AI Governance with Corporate Policies
Lecture 152 Case Study: Integrating AI Governance
Lecture 153 Developing AI Compliance Programs
Lecture 154 Case Study: Navigating Ethical and Regulatory Challenges at TechNova
Lecture 155 Evaluating and Auditing AI Governance
Lecture 156 Case Study: TechNova’s Strategic Approach to Ethical AI Integration
Lecture 157 Section Summary
Section 15: Emerging Issues and Trends in AI Ethics
Lecture 158 Section Introduction
Lecture 159 Impact of AI on Employment and Society
Lecture 160 Case Study: Balancing Innovation, Privacy, and Human Empathy
Lecture 161 Ethical Challenges with Autonomous Systems
Lecture 162 Case Study: Navigating Ethical Challenges in Autonomous Systems
Lecture 163 AI in Law Enforcement and Surveillance
Lecture 164 Case Study: Balancing AI Innovation and Ethics in Metropolis Law Enforcement
Lecture 165 The Future of AI in Medicine and Healthcare
Lecture 166 Case Study: Enhancing Patient Care and Ethics at Evergreen Medical Center
Lecture 167 Preparing for Future Ethical Challenges
Lecture 168 Case Study: Fairness, Privacy, and Transparency Challenges in Public Services
Lecture 169 Section Summary
Section 16: Communicating and Training on AI Ethics
Lecture 170 Section Introduction
Lecture 171 Building an Ethical Culture in AI Development
Lecture 172 Case Study: Embedding Ethics into AI
Lecture 173 Training Employees on AI Ethics and Governance
Lecture 174 Case Study: Cultivating Ethical AI Practices
Lecture 175 Engaging Stakeholders in AI Ethical Practices
Lecture 176 Case Study: Ethical Stakeholder Engagement in AI Healthcare
Lecture 177 Developing Ethical AI Communication Strategies
Lecture 178 Case Study: Crafting Ethical AI Communication
Lecture 179 Assessing Training and Communication Effectiveness
Lecture 180 Case Study: Evaluating AI Ethics Training: InnovateAI’s Strategy and Challenges
Lecture 181 Section Summary
Section 17: Course Summary
Lecture 182 Conclusion
Business leaders aiming to implement responsible AI practices.,AI developers focused on ethical and transparent model design.,Compliance officers managing AI governance policies.,Tech professionals interested in AI ethics and risk management.,Policy makers needing insights into AI regulations and standards.,Data analysts seeking to understand bias and fairness in AI.,Educators and trainers teaching ethical principles in AI development.
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