With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.
MULTIFILE
Artificial Intelligence (AI) is increasingly shaping the way we work, live, and interact, leading to significant developments across various sectors of industry, including media, finance, business services, retail and education. In recent years, numerous high-level principles and guidelines for ‘responsible’ or ‘ethical’ AI have been formulated. However, these theoretical efforts often fall short when it comes to addressing the practical challenges of implementing AI in real-world contexts: Responsible Applied AI. The one-day workshop on Responsible Applied Artificial InTelligence (RAAIT) at HHAI 2024: Hybrid Human AI Systems for the Social Good in Malmö, Sweden, brought together researchers studying various dimensions of Responsible AI in practice.This was the second RAAIT workshop, following the first edition at the 2023 European Conference on Artificial Intelligence (ECAI) in Krakow, Poland.
MULTIFILE
The field of data science and artificial intelligence (AI) is growing at an unprecedented rate. Manual tasks that for thousands of years could only be performed by humans are increasingly being taken over by intelligent machines. But, more importantly, tasks that could never be performed manually by humans, such as analysing big data, can now be automated while generating valuable knowledge for humankind
