Exploring Human and Social Challenges in Data-Driven Life Sciences

How is artificial intelligence (AI) transforming the life sciences, and what ripple effects does this have on humanity and society? These were the central questions explored during a three-day sandpit-style workshop hosted by the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) and The Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society (WASP-HS), at Sigtunastiftelsen on May 13–15.

“The workshop brought together researchers from DDLS and WASP-HS to collaboratively explore the evolving role of artificial intelligence in the life sciences and its many societal implications,” says co-host Erik Kristiansson (Chalmers).

Participants contributed perspectives from the life sciences, computer science, social sciences, and the humanities, creating a unique setting for cross-disciplinary dialogue. Instead of traditional presentations, the format encouraged collaborative work and open exploration around key questions such as: How do we build trust across stakeholders? What counts as “good data”? Who gets to decide in complex, data-driven systems?

Discussions spanned a range of timely topics, from data sharing, consent, and transparency to bias in AI models, regulatory gaps in clinical applications, and how current data practices shape what questions researchers are even able to ask.

“For collaborations to truly succeed, we need meeting places where there is time to listen, test ideas, and challenge each other’s perspectives,” noted co-host Francis Lee (Chalmers/Södertörns Högskola).

The event, which was very successful, concluded with a collective writing exercise in which each participant contributed to a joint reflection on the specific challenges raised during the workshop. These included legal and ethical issues surrounding autonomous systems in healthcare, the establishment of trust in AI-based decision support, emerging forms of human–AI collaboration, and the importance of fostering long-term interdisciplinary communities.