This tutorial presents ongoing research in the field of Responsible Artificial Intelligence (RAI) by introducing core concepts and means of operationalising AI ethics. Attendees will—through both lectures and problem-based learning exercises—get experience in implementing and testing systems for policy compliance.

Here, we introduce the fundamental aspects of RAI by providing a holistic multidisciplinary view. The course structure is such as to introduce to the attendees to the impact intelligent and autonomous systems have on societies and individuals and ongoing state-of-the-art discussions related to ethical, legal, and social aspects of AI. This introduction will be followed by a critical discussion of where accountability and responsibility lie for ethical, legal, and social impacts of these systems, considering decision points throughout the development and deployment pipeline. With this knowledge in mind, students will be introduced to socio-technical approaches the governance, monitoring and control of intelligent systems as tools for incorporating constraints into intelligent system design. Finally, participants apply these skills on a simulated responsible design problem.

The tutorial is divided into following thematic modules:

• Introduction to Responsible AI (lecture – 45 min): Establishes the motivation behind the field of Responsible AI by using real-world use cases related to autonomous operation, algorithmic biases, generation of disinformation, and attempts to escape accountability.

• Responsible AI in practice (exercise – 45 min): Participants will be split up into groups and engage in a value deliberation exercise for a particular problem statement. Given a set of possible solutions to the question, participants should vote which values are promoted or undermined, rank solutions based on this, then discuss the valuation. Finally, the participants are to reconsider their votes after deliberation, and evaluate the new ranking, if changed.

• Responsible AI and ethical dilemmas (interactive lecture – 60 min): A continuation of what Responsible AI looks like in practice, and what exists in terms of standards, governance, and other tooling for responsible development of AI. Participants will take part in deliberating over a number of ethical dilemmas presented, with real-world inspired use-cases where a black-box AI system resulted in discriminatory decisions.

• Explainable AI (lecture – 20 min): Establishes the motivation behind Explainable AI (XAI), what it means for XAI to be human-centred, and how it contributes to the development of Responsible AI.

• Value Deliberation Toolbox – Delft Design for Values Institute
• Dignum, V. (2019). Responsible artificial intelligence: how to develop and use AI in a responsible way (Vol. 2156). Cham: Springer.