Overview

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Based on the best-selling book, Trustworthy AI, by Beena Ammanath. This course equips professionals with the knowledge and tools to build and implement ethical AI systems. It explores the core principles of trustworthy AI, including fairness, transparency, and reliability, and provides practical strategies for integrating these values into business operations. Designed for those navigating the intersection of technology and ethics, it offers actionable insights for responsible AI development. The course covers essential concepts such as fairness, transparency, and the importance of building reliable AI systems that align with ethical guidelines. You'll also discover real-world applications and strategies for integrating these principles into your organization’s AI practices. By the end, you will have gained knowledge on the responsible use of AI and tools for ensuring AI systems are built with trust in mind. What sets this course apart is its focus on applying ethical principles directly to business practices. You’ll gain both theoretical understanding and practical insights to help implement ethical AI practices in real-world scenarios. With real-life case studies and expert-led discussions, this course will equip you to tackle the evolving challenges in AI ethics. This course is designed for business leaders, AI professionals, and technology managers who wish to ensure the ethical development of AI within their organizations. A basic understanding of AI and business processes will help, but deep technical expertise is not required. Copyright © 2022 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Syllabus

A Primer on Modern AI

In this section, we explore AI as mathematical models, not thinking entities, and examine their roles in data science and real-world applications.

Fair and Impartial

In this section, we examine sources of bias in AI systems, evaluate fairness in deployment, and implement ethical practices to ensure equitable and compliant AI applications.

Robust and Reliable

In this section, we explore strategies for ensuring AI robustness and reliability in dynamic environments. Key concepts include testing frameworks, data drift analysis, and continuous learning systems.

Transparent

In this section, we examine transparency in AI, focusing on accountability and stakeholder trust.

Explainable

In this section, we explore AI explainability, focusing on frameworks, black box analysis, and transparent reporting to build trust and ensure compliance in business applications.

Secure

In this section, we explore AI security vulnerabilities, risks of system compromise, and secure deployment practices to ensure safe and trustworthy AI implementation.

Safe

In this section, we explore integrating safety into AI design, analyzing human values, and optimizing objectives to ensure ethical outcomes and prevent harm.

Privacy

In this section, we examine privacy in AI, focusing on data collection methods, regulatory compliance, and informed consent to ensure ethical and trustworthy AI systems.

Accountable

In this section, we explore AI accountability challenges, emphasizing ethical responsibility, transparent decision-making, and frameworks to ensure trust and compliance in AI-driven procurement systems.

Responsible

In this section, we examine evaluating AI systems for ethical alignment, implementing responsible deployment frameworks, and assessing impacts on stakeholder trust.

Trustworthy AI in Practice

In this section, we explore implementing trust dimensions in AI strategies, analyzing trust factors across use cases, and designing consistent governance frameworks for responsible AI deployment.

Looking Forward

In this section, we explore building trustworthy AI through structured governance, emphasizing fair, reliable, and transparent systems aligned with human values and future regulations.