Master's · Marketing & Business Analytics · 12 Weeks

Responsible AI & Algorithmic Governance

Ethics, Auditing, and Regulation in Human and Agent-Mediated Markets

This course examines how organizations design, audit, and govern artificial intelligence systems that interact with consumers and shape market outcomes. Students analyze ethical frameworks, fairness trade-offs, privacy rules, and regulatory regimes, and pair policy analysis with empirical behavioral audits of large language models and autonomous shopping agents. Across the curriculum, artificial intelligence is studied both as an organizational tool that requires oversight and as a market actor whose biases, disclosures, and conflicts of duty must be measured and governed.

Davood Wadi, PhD

Curriculum

Foundations of Responsible AI in Markets
Ethical frameworks for evaluating technology in business, stakeholder responsibility, and why consumer-facing and agent-mediated markets create distinct governance risks.
Algorithmic Bias, Fairness, and Discrimination
Sources of bias across data collection, model training, and deployment, formal definitions of group and individual fairness, and the impossibility of satisfying all fairness criteria simultaneously.
Cultural Bias and Representation in Language Models
Demographic stereotyping, cultural alignment, and consumer ethnocentrism in large language models, and how country-of-origin biases distort product recommendations across global markets.
In preparation
Behavioral Auditing and Statistical Evaluation of AI Systems
Moving beyond static single-sample benchmarks to statistically grounded behavioral audits using Monte-Carlo sampling, token-level probability distributions, and controlled factorial experiments.
In preparation
Transparency, Explainability, and Reasoning Traces
Interpretable models versus post-hoc explanation methods, the faithfulness of chain-of-thought reasoning traces in language models, and what explanations owe to managers, regulators, and consumers.
In preparation
Privacy, Consent, and Data Governance
Data provenance, contextual integrity, privacy-preserving analytics, and research ethics governance for studies involving human participants and synthetic AI respondents.
In preparation
Manipulation, Dark Patterns, and Consumer Autonomy
Digital choice architecture, algorithmic hyper-persuasion, sycophancy, and deceptive interface patterns directed at human consumers and at the AI agents that shop on their behalf.
In preparation
Fiduciary Duty and Conflicts of Interest in Agentic Commerce
Principal-agent relationships when advertising-funded platforms deploy conversational shopping assistants, sponsorship bias, position effects, and why human-centric disclosures fail in agent-mediated markets.
In preparation
Algorithmic Pricing, Collusion, and Fair Competition
Dynamic and personalized pricing, algorithmic price discrimination, tacit coordination among pricing algorithms, and competition policy in automated digital markets.
In preparation
Accessibility, Inclusion, and Human-Centered AI Design
Universal design principles, assistive AI systems for learners and consumers with disabilities, digital divides, and keeping human judgment effective in AI-supported workflows.
In preparation
Global AI Regulation, Standards, and Compliance
Comparative analysis of risk-based AI legislation, consumer protection enforcement, privacy laws, and international management standards governing commercial AI deployment.
In preparation
Organizational AI Governance, Red-Teaming, and Sustainability
Building internal AI ethics and review boards, red-teaming protocols, incident response, environmental and computational resource rationality, and continuous post-deployment monitoring.
In preparation