ABOUT THE COURSE:BackgroundArtificial Intelligence (AI) agents co-existing with human agents generate unique opportunities and challenges for organizations. Recent architectural and algorithmic innovations have empowered AI with capabilities that exceed human capability in several areas. AI’s learning algorithms, trained with data selected by humans also reflect human bias. At the cusp of this transformation which affect individuals, organizations and societies, this course focuses on harnessing value for organizations from AI systems. While the course informs practicing managers on the business value of AI, it also helps them recognize potential biases and human- AI co-existence conflicts. The course also provides a view of emerging AI regulation in select geographies.Learning outcomesTo recognize how AI as a technology can drive organizational goalsTo analyze AI applications in select business functions and domainsTo recognize AI risks from an organizational perspectiveTo develop organizational policy for adopting AI in select domainsINTENDED AUDIENCE: Students seeking applications of AI in business and managementPREREQUISITES: An under graduate having done courses on applied statistics, MIS, and programmingINDUSTRY SUPPORT: AI and innovation wing of all industry sectors, Analytics and data science industry, IT services industry, Manufacturing and services operations and marketing

Syllabus

Week 1-2: Foundations: The artificial neuron, neural networks, training, gradient descent todeep nets, language and vision models, narrow, and general intelligence,generative AI

Week 3-4:AI, strategy and business models: Digital business strategy, enterprise to ecosystems, digital platforms, competition and smart connected products, businessmodels in AI
Week 5-6:Business value of AI: AI-business value mechanism, value creation, AI forbusiness functions-Marketing, Operations, and HR; and business domains-Manufacturing, and Healthcare

Week 7-8:Algorithmic decision making: Machines for decisions, learning algorithms-decision support to decision-making, conversational agents and anthropomorphism, social structure, demographic disparity and prejudice, the spectrum of cognitive biases, social media and the echo chamber effects, the changing role of general managers; interpretability, explainability and decision stakes, accuracy vs interpretability, solutions and limitations

Week 9-10:AI Risks: AI risk and sources, AI bias, types of bias, hallucination and jailbreaking, business-AI alignment; risk management-technological solutions: unlearning and forgetting, robustness checks, debiasing, data sharing and differential privacy

Week 11-12:
Responsible AI and regulation: Fairness and its categories, fair equality of opportunities, philosophy of policy, fairness and policy, model selection for fairness, Governance, and regulation, human values vs market-oriented regulation, AI innovation-regulation trade off, emerging regulations and compliance in select geographies