CIS 202 - Applied Ethics for AI

1. Examine the role of ethics in building responsible AI solutions.
2. Distinguish and describe different principles of Ethical AI. These principles include, but are not limited to, human–centered AI, ensuring transparency, fairness, autonomy, beneficence, non–maleficence, privacy, etc.
3. Examine common ethical pitfalls of AI (such as bias, unintended consequences, non-compliance, privacy violations, etc.) and explore ways to avoid them.
4. Be able to take an ethics-first approach to development using the AI project cycle.
5. Describe what ethical frameworks are and review how to use relevant frameworks for analyzing ethical dilemmas.
6. Apply different ethical frameworks such as bioethics, Kantian ethics, virtue-based ethics, and utilitarianism for analyzing and resolving various ethical dilemmas.
7. Assess the impact of AI solutions and their various ethical implications and develop plans to mitigate unintended consequences.

 
Course Outline:
    I. Introduction to AI Ethics
        I.A. Introduction to Ethics and AI
        I.B. Introduction to Terms – (Ethics vs. Morality)
        I.C. Overview of AI for Good Initiatives & the Importance of Responsible AI

    II. Role of Ethics in Responsible AI
        II.A. Exploring the Role of Ethics in Shaping Responsible AI Practices
        II.B. Ethics in Generative AI

    III. Common Ethical Pitfalls in AI
        III.A. Common Pitfalls and Challenges in Ethical AI Development
            III.A.1. Unintended Consequences
            III.A.2. Bias Against Groups
            III.A.3. Lack of Transparency and Explainability
            III.A.4. Privacy Violations
            III.A.5. Security Vulnerabilities and Non-compliance

    IV. Principles of Ethical AI
        IV.A. Understanding Key Principles for Ethical AI Development and Deployment
            IV.A.1. Fairness and Transparency
            IV.A.2. Building Accountability
            IV.A.3. Ensuring Privacy and Security (Autonomy and Agency)
            IV.A.4. Reliability and Robustness
            IV.A.5. Human-centered AI

    V. Ethics-First Approach in the AI Project Cycle
        V.A. Introduction to AI Project Cycle and Ethics-first Approach
        V.B. Incorporating an Ethics-first Approach Throughout the AI Project Cycle
        V.C. Mapping Ethical Issues in Each Stage of the AI Project Cycle

    VI. Approaches for Designing Ethical AI
        VI.A. Examining Different Approaches and Methodologies for Designing Ethical AI Systems
            VI.A.1. Rule-based Approach
            VI.A.2. Top-down Approach
            VI.A.3. Bottom-up Approach
        VI.B. Discussing the Three Approaches in the Context of Generative AI

    VII. Introduction to Ethical Frameworks
        VII.A. Exploring Different Ethical Frameworks Applicable to AI Development
        VII.B. Factors Influencing Decisions in Ethics
        VII.C. Introduction to Using Different Frameworks for Designing Ethical AI

    VIII. Virtue-Based Ethics and Applying Bio-Ethics Framework in AI
        VIII.A. Applying Bio-ethics Principles to AI Development and Decision-making
        VIII.B. Exploring Virtue Ethics and its Relevance to AI Development

    IX. Utilitarianism and Kantianism for Ethical AI
        IX.A. Applying Utilitarian Ethical Theories to AI Design and Decision-making
        IX.B. Applying Kantian Ethical Theories to AI Design and Decision-making

    X. Code of AI Ethics for Organizations
        X.A. Understanding the Importance and Development of AI Ethics Codes for Organizations
        X.B. Comparing and Contrasting Various AI Ethics Codes at Different Companies

    XI. Data Protection and AI
        XI.A. What is data protection? How does it affect individuals?
        XI.B. Regulations Protecting Rights Based on Geography
        XI.C. Data Protection and Copyright Laws in the Context of AI

    XII. Explainable AI (XAI)
        XII.A. Introduction to Explainable AI
        XII.B. Introduction to Model Cards and Building Trust
        XII.C. XAI Techniques – Model Agnostic Interpretability

    XIII. Impact Assessment of AI Solutions
        XIII.A. What are impact assessments? Introduction to Algorithmic Impact Assessment
        XIII.B. Understanding the Impact of AI Solutions on Society, Economy, and Environment

    XIV. Mitigating Ethical Issues Post Deployment
        XIV.A. Common Ethical Issues Post-deployment
        XIV.B. Strategies for Mitigating Ethical Concerns
        XIV.C. Practical Approaches to Identifying and Mitigating Ethical Challenges

Facilitated Discussion 20%-30%

Facilitated Group Work 40%-50%
Mandatory Course Components:
None
Equivalent Courses:
None