Ethics in AI and Data Science (LFS112) Course Overview

Prerequisites for the "Ethics in AI and Data Science (LFS112)" Course

The "Ethics in AI and Data Science (LFS112)" course is designed to be accessible to individuals from various professional backgrounds. However, to ensure a productive learning experience, it is recommended that participants meet the following minimum prerequisites:

• Basic Understanding of AI and Data Science Concepts: Familiarity with the fundamentals of Artificial Intelligence and Data Science will help you grasp the ethical principles discussed in the course.
• Role in Business, Government, or Technology: The course is best suited for business, government, and technology leaders, as well as data scientists who are involved in building or adopting AI tools.
• Interest in Ethical Practices: A keen interest in learning about ethical frameworks and their application in AI and Data Science initiatives.

These prerequisites are designed to ensure that you can effectively engage with the course material and apply the ethical principles and frameworks discussed to your specific context.

Target Audience for Ethics in AI and Data Science (LFS112)

Introduction:

The Ethics in AI and Data Science (LFS112) course equips leaders and data scientists with the knowledge to embed ethical principles in AI and data science for transparency and trust.

Target Audience:

• Business Leaders
• Government Officials
• Technology Leaders
• Data Scientists
• AI Engineers
• Machine Learning Specialists
• IT Managers
• Innovation Officers
• Compliance Officers
• Risk Management Professionals
• Policy Makers
• Research Analysts
• Academic Researchers
• Data Analysts
• Product Managers in Tech & Data Science Companies

Learning Objectives - What you will Learn in this Ethics in AI and Data Science (LFS112)?

Ethics in AI and Data Science (LFS112)

Introduction

The Ethics in AI and Data Science (LFS112) course aims to equip leaders and data scientists with the knowledge and strategies needed to incorporate ethical principles and frameworks into AI and data science initiatives, focusing on transparency, trust, and responsibility.

Learning Objectives and Outcomes

• Understanding the Current State of Ethics in AI and Data Science
  • Evaluate the importance of ethics, trust, and responsibility in AI and data science applications.
• Defining Artificial Intelligence and Data Science
  • Understand what constitutes Artificial Intelligence and Data Science, and why these fields are crucial in today's technological and business landscapes.
• Ethical Principles and Frameworks
  • Learn about various ethical principles and frameworks that can be applied to AI and data science projects.
• Importance of Transparency
  • Recognize the need for transparency to build trust and drive adoption of AI tools.
• Strategies for Ethical Implementation
  • Identify strategies to integrate ethical principles into AI and data science practices.
• Overcoming Challenges
  • Discuss the challenges faced while implementing ethical practices and how to address them.
• Building Trust in AI Solutions
  • Understand methods to ensure that AI solutions are trustworthy and responsible.