Starting Up

Digital Revolution

Emerging Technologies

Cybersecurity

Essence of Agile

Regenerative Farming

Rethinking Health

AI for built environment

How to have difficult conversations

Introduction to inclusivity for knowledge workers

Focus training for knowledge workers

Feedback for self-organizing teams

Facilitation as a skill for forward looking teams

How not to burn out in the tech industry

Trustworthy AI

Advanced trustworthy AI

Elements of AI for Business

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In this chapter, we’ll take a look at the many ways in which AI is used and how those applications affect our everyday lives, followed by the ethical challenges they may cause. 
We’ll also discuss how trustworthy AI can help solve ethical challenges. Finally, you’ll read about how trustworthy AI can change how businesses work.

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The societal impact of AI

AI’s ethical implications

The dimensions of trustworthy AI

How can (trustworthy) AI transform businesses?

The steps in building AI applications

Trustworthy AI in society and business

In this chapter, we’re going to explore the concept of fairness in AI and data processing. Then, we’ll look at how we define fairness and bias, how an AI system can become biased, and what can be done about it.

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Challenges in implementing fairness in AI

Detecting and mitigating bias and unfairness 

Fairness and accountability

In this chapter, we’ll look at how AI systems are built using various algorithms. Our main goal here is to identify why these AI algorithms are sometimes seen as “black-box” decision-making solutions. After that, we’ll discuss multiple explainable AI methods (XAI) and how XAI interprets the learning process of AI algorithms so that they’re understandable and explainable to people.

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The need for explainable AI

Types of explainable AI

How reliable are XAI explanations?

Explainability

This chapter dives into the resilience of AI systems, a crucial aspect for the successful implementation of trustworthy AI. We start with an overview of the security and privacy challenges that arise in the context of AI and examine the underlying causes for occasional deviations from expected functionality. Furthermore, we explore the potential risks posed by security and privacy issues specific to various stakeholders. Finally, we delve into the fundamental principles and practical implications of privacy laws and regulations in the European Union (EU) to explain their significance for different stakeholders.

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Challenges in AI performance and data integrity

AI security and privacy attacks

Mitigating security risks

AI laws and regulations in EU

Resilience

In this chapter, we’ll first look at some practical tips for developing trustworthy AI in your organization. In the second section, you’ll get to practice all the steps in the implementation of trustworthy AI through a case exercise. Finally, we’ll take a look at what may lie ahead by discussing a few likely future scenarios.

Trustworthy AI in society and business

Fairness and accountability

Explainability

Resilience

Implications