The summerschool starts on Sunday with a welcome reception between 19:00 and 20:30. On Tuesday evening there is a social event in downtown Nijmegen and on Wednesday there will be a walk in the woods followed by a rump session. The summerschool ends on Friday after lunch. The ISP schedule comprises of:
- 9 lectures
- 5 case study sessions
- rump session
- privacy karaoke?
This Year's Programme
Lectures have a coffee/tea break halfway and end with a 15’ Q&A. All lectures, presentations and the rump session take place in the main conference room. The rump session allows students to present their (unfinished) research in an informal setting, using a brief, engaging, presentation. To work on the case studies, please find yourself a comfortable spot somewhere.
Breakfast and lunch are available in the hotel restaurant, and are self-service. Dinner is served in the hotel restaurant as well.
AI in Health: Challenges for Machine Learning and the GDPR
The use and uptake of Artificial Intelligence (AI) has rapidly increased across sectors. In the health domain, AI, particularly in the form of Machine Learning (ML) has emerged as a potentially valuable tool in diagnosis, treatment, and care. Whether in the form of algorithm-driven diagnostics, predictive evaluations, or precision medicine, the ability to perform iterative optimization strategies based on ever-expanding data sets, e.g. pixel evaluation of image data or the processing of sensory data, carries the potential to enhance healthcare in significant ways. However, the use of ML for diagnostic, therapeutic, and public health optimization presents challenges to data protection and privacy. This session examines the challenges that ML modalities present for the GDPR and explores possible ways to address these challenges.
From Agnostic to Agonistic Machine Learning
Machine learning (ML) is agnostic insofar as machines do not know anything in our sense of that term. As machines increasingly make decisions that make a difference, often based on ML, it becomes crucial that those who suffer the consequences of such decisions do not remain agnostic as the potential and the limits of ML. In this lecture I will dive into the assumptions, the design and the implications of machine learning (ML), notably for privacy as the right the protection of the incomputable self. Based on a practical understanding of what ML stands for I will develop a small vocabulary of terms used in the science of ML, some of which have been hyped way beyond their limited meaning in computer science. This vocabulary, in turn, should enable an agonistic debate on the legitimacy of relevant design decisions and their trade-offs, while reinstating the incomputable self as what requires protection in environments that feed on ML decision making.