Assessing Trustworthy AI. Best Practice: AI Medical device for Predicting Cardiovascular Risks
We have used Z-Inspection® to evaluate a non invasive AI medical device which was implemented to assist medical doctors in the diagnosis of cardiovascular diseases.
Assessing Trustworthy AI. Best Practice: Machine learning as a supportive tool to recognize cardiac arrest in 112 emergency calls for the City of Copenhagen.
Jointly with the Emergency Medical Services Copenhagen, we completed the first part of our trustworthy AI assessment.
We use an holistic approach
“Ethical impact evaluation involves evaluating the ethical impact of a technology’s use, not just on its users, but often, also on those indirectly affected, such as their friends and families, communities, society as a whole, and the planet.“
–Peters et al.
Trust between humans and AI systems is integral to promoting the development and deployment of socially beneficial and responsible AI.
Co-design of Trustworthy AI. Best Practice: Deep Learning based Skin Lesion Classifier.
We used Z-Inspection® as an ethically aligned co-design methodology and helped ML engineers to ensure a trustworthiness early design of an artificial intelligence (AI) system component for healthcare.
In cooperation with
German Research Center for Artificial Intelligence GmbH (DFKI)
Assessing Trustworthy AI in times of COVID-19: Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients.
We conducted a self assessment together with the
Department of Information Engineering and Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health – University of Brescia, Brescia, Italy