Current Issues in Ethics and AI Course

Lesson 1 • Mapping the AI Ethics Landscape

Surveys major actors, institutions, and ongoing debates shaping AI ethics globally. Prepares students to navigate real-world ethical discourse.

Lesson 2 • Historical Context of Technology Ethics

Traces ethical debates from industrial automation to modern AI. Situates current issues within a longer arc of technological disruption.

Lesson 3 • What Is AI Ethics

Defines AI ethics as a discipline and distinguishes it from law and policy. Anchors the chapter by establishing shared terminology for all subsequent topics.

Lesson 4 • Values Embedded in AI Systems

Examines how design choices encode values into algorithms and data pipelines. Connects abstract ethics to concrete engineering decisions.

Lesson 1 • Mitigation Strategies and Trade-offs

Covers pre-processing, in-processing, and post-processing debiasing techniques. Evaluates accuracy-fairness trade-offs in deployment contexts.

Lesson 2 • Sources of Algorithmic Bias

Identifies how bias enters AI through data collection, labeling, and model design. Grounds fairness analysis in concrete technical origins.

Lesson 3 • Protected Attributes and Proxy Variables

Examines how removing sensitive attributes fails to prevent discrimination via correlated proxies. Connects legal anti-discrimination concepts to technical realities.

Lesson 4 • Defining Fairness in AI

Contrasts statistical fairness definitions including demographic parity, equalized odds, and calibration. Reveals inherent trade-offs among competing definitions.

Lesson 1 • Key Explainability Techniques

Surveys LIME, SHAP, saliency maps, and decision trees as practical tools. Connects technical methods to ethical requirements for meaningful explanation.

Lesson 2 • The Black Box Problem

Describes opacity in complex models and why it matters for accountability. Establishes the ethical stakes that motivate explainability research.

Lesson 3 • Building Justified Trust in AI

Connects transparency practices to calibrated human trust in AI systems. Synthesizes the chapter by linking explainability to broader accountability goals.

Lesson 4 • Limits and Risks of Explanations

Critiques misleading, incomplete, or gameable explanations. Prevents over-reliance on explainability as a complete ethical solution.

Lesson 1 • Privacy-Preserving AI Techniques

Introduces differential privacy, federated learning, and synthetic data as technical safeguards. Bridges ethical principles with engineering solutions.

Lesson 2 • Data Rights and Individual Control

Covers rights to access, correction, deletion, and portability of personal data. Empowers students to design systems that respect user data rights.

Lesson 3 • Privacy as an Ethical Value

Frames privacy as foundational to autonomy, dignity, and democratic participation. Establishes the normative basis for data rights in AI contexts.

Lesson 4 • AI-Enabled Surveillance Technologies

Surveys facial recognition, behavioral tracking, and predictive profiling as surveillance tools. Connects technical capabilities to civil liberties concerns.

Lesson 1 • Redress and Remedy Mechanisms

Analyzes how affected individuals can challenge and seek remedy for AI-caused harms. Completes the accountability chain from governance to individual recourse.

Lesson 2 • Responsibility Gaps in AI Systems

Identifies how distributed development creates gaps where no actor is held responsible. Motivates the need for explicit accountability structures.

Lesson 3 • Organizational AI Ethics Structures

Examines ethics boards, review processes, and red-teaming as internal governance tools. Connects governance theory to practical organizational implementation.

Lesson 4 • AI Governance Frameworks

Surveys risk-based, rights-based, and sector-specific governance approaches. Equips students to evaluate and apply governance models in their organizations.

Lesson 1 • Harm Prevention and Safe Design

Covers safety-by-design principles, human oversight requirements, and kill-switch mechanisms. Synthesizes risk knowledge into actionable prevention strategies.

Lesson 2 • Risk Assessment Methodologies

Introduces failure mode analysis, red-teaming, and probabilistic risk assessment for AI. Equips students to conduct structured pre-deployment risk evaluations.

Lesson 3 • High-Stakes Deployment Contexts

Analyzes ethical risk amplification in healthcare, criminal justice, and critical infrastructure. Applies risk frameworks to domains where errors cause severe harm.

Lesson 4 • Robustness and Reliability

Examines distribution shift, adversarial attacks, and model degradation as safety threats. Connects technical robustness to ethical obligations of reliable performance.

Lesson 1 • Designing Effective Human Oversight

Provides frameworks for designing oversight mechanisms that are genuinely effective. Synthesizes the chapter by translating autonomy ethics into system design.

Lesson 2 • Spectrum of AI Autonomy

Maps AI systems from decision support to full autonomy and the ethical stakes at each level. Establishes the conceptual range for human-AI authority allocation.

Lesson 3 • AI in Consequential Social Decisions

Analyzes AI use in hiring, lending, sentencing, and benefits allocation. Evaluates when automation undermines fairness and individual rights.

Lesson 4 • Autonomous Weapons and Lethal AI

Examines ethical debates around lethal autonomous weapons systems and accountability gaps. Applies autonomy principles to the most consequential deployment domain.

Lesson 1 • AI, Labor, and Economic Justice

Analyzes automation's distributional effects on workers, wages, and economic inequality. Connects AI deployment decisions to broader social justice obligations.

Lesson 2 • Developing an Ethical AI Strategy

Synthesizes course content into a strategic framework for organizational AI ethics leadership. Equips students to champion ethical AI at a systems level.

Lesson 3 • Long-Term and Existential AI Risks

Surveys arguments about misalignment, value lock-in, and catastrophic AI risk. Introduces students to long-termist perspectives and their critics.

Lesson 4 • Global AI Governance Challenges

Examines geopolitical competition, regulatory fragmentation, and governance gaps in AI. Prepares students to reason about international coordination challenges.