Advancing AI Ethics in Engineering Curricula in Europe: A Case Study Approach

The Erasmus+ Ethical Engineer project aims to enhance AI education by integrating ethical, social, and legal aspects into engineering training, promoting truthful AI in Europe. In the paper, we present the explored case studies and the methodology for their development. AI ethics is explored through the following case studies. AI in healthcare is examined through fall detection systems for vulnerable populations, the potential for data bias in personalized medical devices, and the ethical implications of off-label device use, such as continuous artificial hearts. The use of AI in witness testimonies is considered through the example of the AIWitness chatbot designed to automate witness statements. Ethical considerations surrounding facial recognition are explored in the context of airport boarding, using a hypothetical scenario based on Brighton International Airport's pilot program, FaceBoard. The project also considers self-driving taxis' ethical and practical implications, using a hypothetical scenario in San Francisco. The ecological impact of generative AI is analyzed, referencing studies from Stanford University on energy consumption and water usage for cooling systems. The ethical challenges of AI in marketing processes are addressed, including the generation of content like text, images, and voice, along with concerns about job displacement, fake content creation, and authorship issues. The use of AI in assessing historic photos is examined, focusing on selection bias, the potential for surfacing insulting content, the need for transparency, and the potential prioritization of profit over ethical considerations. Finally, the paper addresses the pervasive issue of bias in data, particularly within the field of data science. The main contribution of the paper is the development of a structured template for creating AI ethics case studies, which we already tested with smaller groups of teachers and students and refined based on their feedback.