SEAMS 2024
Mon 15 - Tue 16 April 2024 Lisbon, Portugal
co-located with ICSE 2024

Recent advances in generative AI and machine learning have stirred up fears about the unbridled adoption of autonomous, self-adaptive decision mechanisms in socio-technical systems. This vision paper explores the critical relationship between software-intensive systems and the empowerment of humans as individuals and society. We highlight the need for human empowerment within the context of self-adaptive socio-technical systems (SASTSs), which require mechanisms for balancing of diverse needs, values, and ethics on the individual, community, and societal levels. We propose an architecture comprised of Connector and Mediator elements, and third-party auditing, to support interactions and ensure preservation of human needs, values, and ethics. We use an example of Robot-Assisted A&E Triage system to motivate and illustrate our work and discuss some open challenges for future research.

Tue 16 Apr

Displayed time zone: Lisbon change

16:00 - 17:30
Session 8: Human Aspects + Closing + SEAMS 2025Research Track at Luis de Freitas Branco
Chair(s): Genaina Rodrigues University of Brasilia
16:00
25m
Talk
Explanation-driven Self-adaptation using Model-agnostic Interpretable Machine LearningFULL
Research Track
Francesco Renato Negri Politecnico di Milano, Niccolò Nicolosi Politecnico di Milano, Matteo Camilli Politecnico di Milano, Raffaela Mirandola Karlsruhe Institute of Technology (KIT)
16:25
15m
Talk
Human empowerment in self-adaptive socio-technical systemsSHORT
Research Track
Nicolas Boltz Karlsruhe Institute of Technology (KIT), Sinem Getir Yaman University of York, UK, Paola Inverardi , Rogério de Lemos University of Kent, UK, Dimitri Van Landuyt KU Leuven, Belgium, Andrea Zisman The Open University
16:40
15m
Talk
Towards Understanding Trust in Self-adaptive SystemsSHORT
Research Track
Dimitri Van Landuyt KU Leuven, Belgium, David Halasz Masaryk University, Stef Verreydt DistriNet-KU Leuven, Danny Weyns KU Leuven
16:55
15m
Talk
SafeDriveRL: Combining Non-cooperative Game Theory with Reinforcement Learning to Explore and Mitigate Human-based Uncertainty for Autonomous VehiclesSHORT
Research Track
Kenneth Chan Michigan State University, Sol Zilberman Michigan State University, Nicholas Polanco Michigan State University, Betty H.C. Cheng Michigan State University, Josh Siegel Michigan State University
17:10
20m
Talk
Closing
Research Track