Ethics in AI Design

Week 1 Introduction to the Ethical challenges with AI.

A first overview of how ethics interacts with the design and use of AI systems.

Overview of how we will tackle these challenges during the course: introduction of the Design for Values methodology and its application to AI design.

We look into identifying values of different stakeholders and on the methods to then operationalize these, applied to our central case study in the healthcare domain.

Week 2 Trustworthiness of AI systems, accuracy and explainability.

When is an AI system trustworthy, and how does this interact with requirements for accuracy and explainability? Should systems always be explainable? What do we focus on with respect to accuracy and reliability/robustness of systems? In addition, we briefly look into what is technically available. How explainable are these systems? What tools are available to improve the explainability of AI systems, and when do we use them?

Week 3 Bias in data and algorithmic fairness.

We will discuss both philosophical conceptions of fairness and bias as well as the connection to concrete metrics and tools that can be used to monitor and correct for biases. Investigate which (statistical) biases are problematic and what appropriate steps are to tackle them.

Week 4 Accountability and human oversight.

Who is responsible when mistakes are made with AI systems? What organisational/socio-technical design is needed to ensure responsible use of AI? Human oversight is discussed and the notion of meaningful human control is introduced. We then look at how oversight can be implemented in different ways, both technically (logs, audits, etc) and in the organisation (the role we give to an AI system).

Week 5 Value conflicts: what to do when different ethical values are difficult to realise at the same time?

Final assignment: for a new case, conduct the translation of ethical values into design requirements yourself.