Responsible and Ethical AI

Overview

In this course, we will investigate the ethical challenges in Artificial Intelligence (AI) systems. The focus of this course is on preparing students with the knowledge and practical approaches necessary in designing reliable and ethical AI systems that are responsible and trustworthy. Key topics covered include: • Bias and Fairness in AI and Machine Learning • Nature of data privacy and AI Risks • Understanding AI regulations. • Frameworks for building truly trustworthy and responsible AI. There are 2 hands-on labs in this course. You need knowledge of python and basics of AI model development.

Syllabus

AI and Dependencies

In this module, we will discuss the language of data and how to use data to make a business decision. We will discuss how an AI-driven culture helps organizations make better and more effective decisions.

Bias in AI

In this module, we will discuss various types of bias that can influence AI model decisions and explore strategies to mitigate these challenges. We will also examine other AI risks that impact the development of ethical AI systems. The module also covers how bias can impact the outcome of the results and misrepresent the data, violate company policies, and damage an organization’s reputation.

AI Transparency and Explainability

We will discuss a comprehensive framework for developing reliable, responsible, and ethical AI systems. We will center on transparency and explainability, understanding how to make AI decisions interpretable and trustworthy to users and stakeholders. The discussion will cover key areas such as data governance, regulatory compliance, privacy concerns, and transparency. By addressing these critical factors, we aim to explore how organizations can design and implement AI systems that are not only effective but also trustworthy, fair, and aligned with ethical standards.

Designing Reliable Responsible AI

In this module, we will explore various AI standards and frameworks, including the NIST AI Risk Management Framework, as well as key regulatory frameworks such as the EU AI Act, GDPR, and other emerging international AI regulations. We will examine the growing importance of these standards in guiding responsible AI development across different industries and jurisdictions, and discuss how global variations in regulatory approaches impact the design, deployment, and governance of AI systems.