Overview

This course explores the intersection of artificial intelligence (AI), machine learning (ML), and responsible business practice in our increasingly AI-driven economy. Participants establish foundational understanding of AI and ML concepts, their real-world applications, and factors driving their widespread adoption across industries. The course presents the machine learning process—from data collection and preparation through model development and evaluation—providing practical insights into how data transforms into actionable business insights. Significant attention is dedicated to algorithmic bias, a critical challenge that can undermine system effectiveness and create unintended disparities in AI applications. Through examination of real-world cases across sectors such as recruitment, healthcare, and financial services, participants learn to identify different types of bias—historical bias, representation bias, and measurement bias—and understand their business implications. The course concludes with practical strategies for bias detection and mitigation, along with governance frameworks for AI deployment. Participants gain the knowledge needed to build AI systems that work effectively for diverse populations while delivering reliable business value, preparing future leaders to harness AI's transformative potential while managing its risks and ensuring broad accessibility. This course is best suited for individuals seeking to advance their careers through skill-building, industry application, and network expansion. Whether aiming for a promotion, transitioning to a new career, or growing one’s professional skills, learners will gain valuable insights into how they can contribute to their organizations and articulate those ideas with peers, recruiters, and other stakeholders.

Syllabus

Unraveling the World of Artificial Intelligence and Machine Learning

This introductory module demystifies artificial intelligence and machine learning by exploring their fundamental concepts, the differences between them, and their real-world applications that impact our daily lives. Through clear explanations and concrete examples, you'll gain essential knowledge about how these technologies function across various contexts, building a foundation for understanding their strategic importance and preparing you for deeper exploration of their mechanisms and ethical implications in later modules.

Demystifying the Machine Learning Process

This module provides an overview of the machine learning process, exploring the four essential phases: data collection, data preparation, model development, and model evaluation. Through understanding these foundational phases, learners will gain practical knowledge that enables effective collaboration with technical teams, better evaluation of AI initiatives, and identification of machine learning opportunities within their organizations.

When Algorithms Get It Wrong: The Hidden World of Bias

This module examines how algorithmic bias emerges in AI systems, revealing why even sophisticated machine learning algorithms can produce unfair or inaccurate results. Students explore three critical types of bias—historical, representation, and measurement—through real-world examples spanning healthcare, hiring, and financial services. By understanding how biases infiltrate AI systems and learning to identify their warning signs, students develop the analytical skills needed to assess algorithmic fairness and evaluate potential solutions in business contexts.

Building Fairer AI - Strategies for Reducing Algorithmic Bias

This module equips students with practical tools to address algorithmic bias in business applications. Through examination of bias mitigation techniques—from synthetic data generation to algorithmic modifications that ensure equal performance across demographic groups—students learn how to build more inclusive AI systems. The module also explores governance frameworks, comparing industry self-regulation with government oversight approaches such as the EU AI Act, preparing future leaders to navigate the evolving landscape of responsible AI deployment while maintaining competitive advantage.