AI Ethics Business Course Syllabus

Overview: This course provides a comprehensive introduction to AI ethics in business, designed for beginners interested in responsible AI adoption. Over approximately 15-20 hours, learners will explore foundational AI concepts, ethical challenges, and real-world applications across six modules. The curriculum combines theoretical knowledge with case studies and practical assessments to build awareness of ethical AI practices relevant to modern organizations.

Module 1: Foundations of Computing & Algorithms

Estimated time: 3 hours

• Case study analysis with real-world examples
• Assessment through quiz and peer-reviewed assignment
• Interactive lab: Building practical solutions

Module 2: Neural Networks & Deep Learning

Estimated time: 3-4 hours

• Discussion of best practices and industry standards
• Case study analysis with real-world examples
• Review of tools and frameworks commonly used in practice

Module 3: AI System Design & Architecture

Estimated time: 4 hours

• Assessment: Quiz and peer-reviewed assignment
• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice
• Hands-on exercises applying AI system design & architecture techniques

Module 4: Natural Language Processing

Estimated time: 1-2 hours

• Introduction to key concepts in natural language processing
• Review of tools and frameworks commonly used in practice
• Assessment: Quiz and peer-reviewed assignment
• Discussion of best practices and industry standards

Module 5: Computer Vision & Pattern Recognition

Estimated time: 2 hours

• Introduction to key concepts in computer vision & pattern recognition
• Review of tools and frameworks commonly used in practice
• Assessment: Quiz and peer-reviewed assignment
• Interactive lab: Building practical solutions

Module 6: Deployment & Production Systems

Estimated time: 2-3 hours

• Discussion of best practices and industry standards
• Introduction to key concepts in deployment & production systems
• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice

Prerequisites

• Familiarity with basic business concepts
• Interest in AI and its ethical implications
• No technical background required

What You'll Be Able to Do After

• Apply computational thinking to solve complex engineering problems
• Understand core AI concepts including neural networks and deep learning
• Design algorithms that scale efficiently with increasing data
• Build and deploy AI-powered applications for real-world use cases
• Implement prompt engineering techniques for large language models