Co-design and Assessing Trustworthy AI. Best Practices

We apply Z-Inspection® to real use cases.

Assessing Trustworthy AI. Best Practice: AI Medial 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.

  Assessment Completed.

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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.

Ethical impact evaluation

“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.

Co-design of Trustworthy AI. Best Practice: Deep Learning based Skin Lesion Classifier.

We use an ethically aligned co-design methodology 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. Best Practice: Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients.

In cooperation with

Department of Information Engineering and Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health – University of Brescia, Brescia, Italy