Philosophy & Ethics (PE)
Data science aspires to change society by creating value. With this aspiration comes the need to reflect on how data-based technologies and services can be designed and implemented responsibly. The P&E group has deep expertise in ethics and the philosophy of science, and applies this expertise to understand technological developments from a societal and humanistic perspective. Our research in the area of data science focuses on responsible innovation, an approach that aims to “identify the ethical and societal aspects of technological innovations at an early stage so that these can be taken into account in the design process” (NWO). We work closely with other
departments on projects where philosophical insight can help data science realize value more responsibly. Some key concepts that we work with are:
- Autonomy and privacy (data sharing, quantified self, ubiquitous sensors)
- Responsibility (networked decision-making, social robotics)
- Sustainability (smart meters, mobility, logistics)
- Trust and trustworthiness (data sharing, personal data analytics, electronic coaching)
The group has extensive experience with multidisciplinary responsible innovation projects.
We’ve created a new conceptual framework for moral responsibility which makes it possible, by thinking ahead, to solve the ‘problem of many hands’. It can assist those who are concerned with practical issues of responsibility distribution in institutions, especially networked systems.
Together with the Rathenau Institute, we’ve provided consultancy on the future of the traffic system in the Netherlands. The report explores the potential of smart mobility, including ICT and persuasive technologies to influence the driver’s behavior in order to improve safety and sustainability, and minimize congestion.
The book Just Ordinary Robots identifies social and normative questions about robotics to be addressed in the short and long term, and highlights key points to be discussed in the public sphere by politicians and policy makers.
- Quantified self and self-tracking:
Project identifies ethical concerns raised by surveillance, quantification, and enhancement dimensions of self-tracking technologies. Issues include privacy, profiling, and preventing threats to free citizens’ right to an “open future”.
- Value trade-offs of interoperable big data in medical contexts:
Project develops a framework for analyzing value trade-offs associated with rendering cloud-based big data sets interoperable for the sake of health. The framework will provide input for interoperable information system design and specification.
- Mobile support systems for behavior change:
Philips, NWO-MVI Grant
Multidisciplinary project identifies social and technological innovations that increase intrinsic motivation and trust in the use of personalized, data-enabled coaching technologies, and rethinks the practice of informed consent in mobile health.