In order to give you a better start for your AI brainstorming session, let us look at eight problem types which can be solved with AI solutions. This list is of course not finite or complete, but it covers big problems many companies face. …

This chapter should give you a clear guidance on how to start your first project. Rule #1: The project should be short and successful (max 3 months). There is always skepticism for new ideas. And this skepticism or “naturally expected non-buy-in” needs to be addressed as a big risk for…

In this chapter we are talking about the non-technical methods to realize the seven requirements of “Trustworthy AI”. In comparison to the last chapter, they all have a non-technical, regulatory and organizational character. …

As we have seen in the sections above, we have the four fundamental principles of Trustworthy AI ( respect of human autonomy, prevention of harm, fairness and explicability). Out of these principles we derived the seven key requirements (human agency and oversight, technical robustness and safety, privacy and data governance…

The last requirement “Accountability” is derived from the meta-principle of fairness and this requirement is probably the most important one for business leaders and governance officers, because it delivers clear KPIs for AI systems. It is important to note that accountability entails the complete life cycle of the AI system…

This requirement extends the previous requirement “diversity, non-discrimination and fairness” and is deducted from the overall principle of fairness and prevention of harm. It desires to include the broader society into the circle of stakeholders. What does this actually mean? Sustainable and environmentally friendly AI First and foremost, the complete supply chain of developing, deploying…

Before we discuss today’s fifth requirement, we need to define certain termini to not create confusion. Discrimination Discrimination has been defined by the EU Council Directive 2000/78/EC of 27 November 2000 which established a general framework for equal treatment in employment and occupation. The Directive’s purpose was “to lay down a…

Today we are talking about the fourth requirement: Transparency. Transparency can be directly linked to the principle of explicability, which we talked about earlier. Transparency of AI should not only cover transparency of the model, but also of the complete data stack and data processing pipeline (data engineering, filtering, aggregation)…

Privacy and data protection Today we are talking about the third requirement: privacy and data governance. Privacy is always closely linked to the previous requirement “Technical robustness and safety” while data governance focuses on the quality and integrity of the data used. The true power of artificial intelligence is the inference of traits, behaviours…