AI+ Ethics™
Navigate the Intersection of AI and Ethics in Business Landscape
This course provides a comprehensive guide to responsible AI, teaching learners how to master ethical AI practices aligned with business and societal values. Participants gain hands-on experience managing compliance, transparency, and AI-driven decision-making to mitigate risks effectively. The program also emphasizes strategic guidance, showing how to integrate ethical practices into AI adoption and leadership initiatives. Finally, learners develop skills to build organizational trust and credibility, ensuring AI deployments enhance reputation while delivering value responsibly.
Enrollment Fee:
MUR 6,045
What You'll Learn
- Course Introduction
1.1 Introduction to Ethical Considerations in AI
1.2 Understanding The Societal Impact of AI Technologies
1.3 Strategies for Conducting Social and Ethical Impact Assessments
2.1 Exploration of Biases in Data and Algorithms
2.2 Strategies for Mitigating Bias and Ensuring Fairness in AI Systems
3.1 Importance of Transparent AI Systems
3.2 Techniques for Explaining AI Models to Diverse Stakeholders
3.3 Guided Projects on Designing and Analysis of AI Systems with Ethical Considerations
Study frameworks for holding organizations accountable for the ethical use of AI.
Why it matters: Ensures ethical AI deployment and helps mitigate the consequences of potential misuse or harm.
5.1 Concepts of Accountability in AI Development and Deployment
5.2 Responsibilities of AI Practitioners and Organizations
6.1 Overview of Relevant Laws and Regulations Pertaining to AI
6.2 Understanding the Global Regulatory Issues for AI Technologies
6.3 Case Studies: GDPR Compliance
6.4 Legal Compliance of AI Tools
7.1 Introduction to Frameworks for Making Ethical Decisions in AI
7.2 Case Studies and Applications of Ethical Decision-Making
7.3 Use of Simulation Platforms in Ethical Decision-Making
8.1 Principles and Functions of International AI Governance
8.2 Best Practices for Integrating AI Ethics into Organizational Policies
8.3 Case Studies on AI Governance
9.1 Explore Standards: IEEE’s Ethically Aligned Design
9.2 Comparative Case Studies on Standard Implementations
9.3 Tools for Evaluating AI Systems Against Global Standards
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Prerequisites
- Basic knowledge of artificial intelligence, machine learning concepts, and their applications.
- Understanding of the social, cultural, and political implications of AI technologies.
- Understanding of professional ethics, including honesty, integrity, and responsibility.
- Exposure to real-world case studies that highlight ethical dilemmas in AI, promoting practical understanding.
- Ability to critically assess AI technologies and make ethical decisions in designing, deploying, and managing AI systems.
- Familiarity with relevant laws, regulations, and industry standards that govern AI usage.
Exam Details
Duration:
90 minutes
Passing Score:
70% (35/50)
Format:
50 multiple-choice/multiple-response questions
Delivery Method:
Online via proctored exam platform (flexible scheduling)
Unlock Self-Paced Online Learning
- Unlock Self-Paced Online Learning
- Access learning anytime, anywhere, with built-in quizzes to measure progress.
- Enrollment Fee: MUR 6,045