Responsible AI Innovation Group
"Responsible AI Innovation requires addressing ethical and societal implications of technology in ways that are robust, human-centred and compatible with the realities of society, the industrial sector and policy making. We combine state-of-the-art research with proven expertise on technological translation, governance of emerging technology and multi-stakeholders engagement to promote AI technology for the common good."
Fields of Expertise
- Responsible Research and Innovation
- Ethically aligned design of intelligent and autonomous systems
- Governance of AI and Neurotechnology
The "Responsible AI Innovation" (RAI) group focuses on technical, governance and ethical aspects of AI-supported innovation. The research focus of the group is to identify technical and non-technical approaches that enable organizations to successfully translate AI-technologies into solutions that promote economical and societal good. We have a special interest in applications that have high impact on humans, and society such as health, neuroscience, human-machine interaction and machine-based decision making. Our work address end-to-end governance factors that influence innovation ranging from organisational governance, regulatory and certification requirements, socio-technical standards, Trustworthy AI and human-centred technology.
As responsible Innovation requires effective coordination of multiple stakeholders and fields of discipline, RAI collaborates with an extensive network of collaborations with national and intrnational organizations including CLAIRE, ADRA, SATW IEEE Standards Organization, IEEE Brain, The Geneva Center for Security Policy, GESDA, OECD, and the Institute for Neuroethics.
The Responsible AI Innovation group was created in September 2023, before that date this line of research was performed within the MPC group. Some of the projects started prior to that date are:
Services
- Insight: keynotes, trainings, public outreach, scientific diplomacy
- AI consultancy: workshops, expert support, advise, technology and ethical assessment
- Research and development: small to large-scale collaborative projects, third party-funded research, student projects, commercially applicable prototypes
Team
Head of Research Group
Projects
Unfortunately, no list of projects can be displayed here at the moment. Until the list is available again, the project search on the ZHAW homepage can be used.
Publications
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Billeter, Yann; Denzel, Philipp; Chavarriaga, Ricardo; Forster, Oliver; Schilling, Frank-Peter; Brunner, Stefan; Frischknecht-Gruber, Carmen; Reif, Monika Ulrike; Weng, Joanna,
2024.
MLOps as enabler of trustworthy AI [paper].
In:
2024 11th IEEE Swiss Conference on Data Science (SDS).
11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.
IEEE.
pp. 37-40.
Available from: https://doi.org/10.1109/SDS60720.2024.00013
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Denzel, Philipp; Brunner, Stefan; Billeter, Yann; Forster, Oliver; Frischknecht-Gruber, Carmen; Reif, Monika Ulrike; Schilling, Frank-Peter; Weng, Joanna; Chavarriaga, Ricardo; Amini, Amin; Repetto, Marco; Iranfar, Arman,
2024.
Towards the certification of AI-based systems [paper].
In:
2024 11th IEEE Swiss Conference on Data Science (SDS).
11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.
IEEE.
pp. 84-91.
Available from: https://doi.org/10.1109/SDS60720.2024.00020
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Weng, Joanna; Denzel, Philipp; Reif, Monika Ulrike; Schilling, Frank-Peter; Billeter, Yann; Frischknecht-Gruber, Carmen; Brunner, Stefan; Chavarriaga, Ricardo; Repetto, Marco; Iranfar, Arman,
2024.
Certification scheme for artificial intelligence based systems [paper].
In:
34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-30549
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Iwane, Fumiaki; Porssut, Thibault; Blanke, Olaf; Chavarriaga, Ricardo; Millan, Jose Del R.; Herbelin, Bruno; Boulic, Ronan,
2024.
Journal of Neural Engineering.
21(2), pp. 026016.
Available from: https://doi.org/10.1088/1741-2552/ad2c02
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Denzel, Philipp; Brunner, Stefan; Luley, Paul-Philipp; Frischknecht-Gruber, Carmen; Reif, Monika Ulrike; Schilling, Frank-Peter; Amini, Amin; Repetto, Marco; Iranfar, Arman; Weng, Joanna; Chavarriaga, Ricardo,
2023.
A framework for assessing and certifying explainability of health-oriented AI systems.
In:
Explainable AI in Medicine Workshop, Lugano, Switzerland, 2-3 November 2023.