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In this course, you will :
- Learn about artificial intelligence and the strategies used to transform businesses in order to gain a competitive advantage. Y investigate the numerous applications of AI in the enterprise and the tools available to lower the barriers to AI use. You will gain a better understanding of the purpose, function, and use-cases of explainable AI.
- As faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a larger scale, this course will also provide you with the tools to build responsible AI governance algorithms.
- Examine AI in the organisational structure, how AI is important in change management, and the risks associated with AI processes.
Syllabus :
1. Economics of AI
- AI- Driven Business Transformation
- Developing a Portfolio
- Lowering Barriers for AI Use
- Economics of AI: Software
- Economics of AI: Skills
- Economics of AI: Compute
- Economics of AI: Data
- Economics of AI: AutoML
- Economics of AI: Auto ML Hubris
- Economics of AI: Competitive Implications
- Interview with Apoorv Saxena
- AI in the Organization Structure
- Interview With Barkha Saxena
2. AI Innovation
- Is AI and Data Analytics Suited for Innovation?
- AI and Process Innovation
- Product Innovation
- Different Types of Product Innovation
- Organization Factors
- Dispersion of Employees
- Managerial Practice
- AI and Drug Example
3. Algorithmic Bias and Fairness
- Risks with AI
- Algorithmic Bias and Fairness
- Manipulation
- Data Protection
- Interview with Yogesh Mudgal
4. AI Governance and Explainable AI
- AI Governance
- AI Ethics Principles
- Explainable AI: What is Explainable AI?
- Explainable AI: Examples of When Explainability is Important
- Explainable AI: Tradeoffs Between Interpretability and Performance
- Explainable AI: Approaches to Explainable AI
- Explainability and the Law
