Course Overview
The AI+ Ethics Certification is an industry-oriented program enabling professionals to distinguish themselves in ethically utilizing emerging AI technologies. Business and government organizations actively seek ethics professionals to mitigate risks and guide decision-making in AI application design. The brand's values and financial impacts resulting from ethical violations can significantly affect an organization's image. Ethical leaders play a crucial role in implementing strategies to promote fairness, minimize risks, and uphold ethical standards, ensuring the overall well-being of their organizations.
Prerequisites
- Basic grasp of ethical principles and moral reasoning
- Interest in how AI impacts society and daily life, and openness to change
- Willingness to understand AI ethical frameworks and guidelines
Course Objectives
- Strategic Thinking
- Learners will examine AI technologies and their commercial consequences, which involves planning strategically and make AI integration decisions for their organizations.
- Ethical-decision making skills
- Students will understand the to negotiate AI's complex ethical issues. This includes identifying ethical issues, use ethical decision-making frameworks, and hold AI initiatives accountable.
- Project Management
- Participants will learn to develop, implement, and monitor AI initiatives while addressing bias, fairness, transparency, privacy, and security.
- Compliance and Governance norms and practices
- Students will learn about AI law and regulation, which involves ensuring AI systems comply with laws and regulations and use governance best practices to reduce risks and preserve transparency and accountability.
Outline: AI+ Ethics (AIET)
Module 1: Overview of AI Ethics & Societal Impact
- 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
Module 2: Bias and Fairness in AI
- 2.1 Exploration of Biases in Data and Algorithms
- 2.2 Strategies for Mitigating Bias and Ensuring Fairness in AI Systems
Module 3: Transparency and Explainable AI
- 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
Module 4: Privacy and Security Issues in AI
- 4.1 Examination of Privacy Concerns Related to AI
- 4.2 Strategies for Ensuring the Security of AI Systems and Data
Module 5: Accountability and Responsibility
- 5.1 Concepts of Accountability in AI Development and Deployment
- 5.2 Responsibilities of AI Practitioners and Organizations
Module 6: Legal and Regulatory Issues
- 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
Module 7: Ethical Decision-Making Frameworks
- 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
Module 8: AI Governance & Best Practices
- 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
Module 9: Global AI Ethics Standards
- 9.1 Explore Standards like IEEE’s Ethically Aligned Design
- 9.2 Comparative Case Studies on Standard Implementations
- 9.3 Tools for Evaluating AI Systems Against Global Standards