After several years of applied research work, we produced two full reports containing the lessons we have learned and a list of practical suggestions:

Cite as: arXiv:2206.09887 [cs.CY]. [v2] Tue, 28 Jun 2022 14:23:47 UTC (465 KB)

The full report is available on arXiv. 

You can download the full report as .PDF

– Lessons Learned from Assessing Trustworthy AI in Practice.

Digital Society (DSO),  2, 35 (2023). Springer

Roberto V. Zicari, John Brodersen, James Brusseau, Boris Düdder, Timo Eichhorn, Todor Ivanov, Georgios Kararigas , Pedro Kringen, Melissa McCullough, Florian Möslein, Karsten Tolle, Jesmin Jahan Tithi, Naveed Mushtaq, Gemma Roig , Norman Stürtz, Irmhild van Halem, Magnus Westerlund.
IEEE Transactions on Technology and Society,
VOL. 2, NO. 2, JUNE 2021

Print ISSN: 2637-6415
Online ISSN: 2637-6415
Digital Object Identifier: 10.1109/TTS.2021.3066209
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Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier.

Roberto V. Zicari, Sheraz Ahmed, Julia Amann, Stephan Alexander Braun, John Brodersen, Frédérick Bruneault, James Brusseau, Erik Campano, Megan Coffee, Andreas Dengel, Boris Düdder, Alessio Gallucci, Thomas Krendl Gilbert, Philippe Gottfrois, Emmanuel Goffi, Christoffer Bjerre Haase, Thilo Hagendorff, Eleanore Hickman, Elisabeth Hildt, Sune Holm, Pedro Kringen, Ulrich Kühne, Adriano Lucieri, Vince I. Madai, Pedro A. Moreno-Sánchez, Oriana Medlicott, Matiss Ozols, Eberhard Schnebel, Andy Spezzatti, Jesmin Jahan Tithi, Steven Umbrello, Dennis Vetter, Holger Volland, Magnus Westerlund and Renee Wurth.

Front. Hum. Dyn. |Human and Artificial Collaboration for Medical Best Practices, July 13, 2021

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Audio file generated by notebookLM (based on the article)

On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

Roberto V. Zicari • James Brusseau • Stig Nikolaj Blomberg • Helle Collatz Christensen • Megan Coffee • Marianna B. Ganapini • Sara Gerke • Thomas Krendl Gilbert • Eleanore Hickman • Elisabeth Hildt • Sune Holm • Ulrich Kühne • Vince I. Madai • Walter Osika • Andy Spezzatti • Eberhard Schnebel • Jesmin Jahan Tithi • Dennis Vetter • Magnus Westerlund • Renee Wurth • Julia Amann • Vegard Antun • Valentina Beretta • Frédérick Bruneault • Erik Campano • Boris Düdder • Alessio Gallucci • Emmanuel Goffi • Christoffer Bjerre Haase • Thilo Hagendorff • Pedro Kringen • Florian Möslein • Davi Ottenheimer • Matiss Ozols • Laura Palazzani • Martin Petrin • Karin Tafur • Jim Tørresen • Holger Volland • Georgios Kararigas

Front. Hum. Dyn., Human and Artificial Collaboration for Medical Best Practices, 08 July 2021 |

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This pilot project of the Z-inspection® initiative aims at assessing the use of Generative AI in higher level education considering specific use cases.

For this pilot project, we will assess the ethical, technical, domain-specific (i.e. education) and legal implications of the use of Generative AI-product/service within the university context.

We follow the UNESCO guidance for policymakers on Generative AI and education. In particular the policy recommendation: Pilot testing, monitoring and evaluation, and building an evidence base.

Participants

– Affiliated Labs

– Members of the Z-inspection® initiative

– Ministries and Universities

– Specialized agencies

– Others

Approach

An interdisciplinary team of experts will assess the trustworthiness of Generative AI for selected use cases in High Education using the Z-Inspection® process.

The polygenic risk score (PRS) is an important method for assessing genetic susceptibility to traits and diseases. Significant progress has been made in applying PRS to conditions such as obesity, cancer, and type 2 diabetes. Studies have demonstrated that PRS can effectively identify individuals at high risk, enabling early screening, personalized treatment, and targeted interventions.

 One of the current limitations of PRS, however, is a lack of interpretability tools. To address this problem, at team of researchers at the Graduate School of Data Science at the Seoul National University introduced eXplainable PRS (XPRS), an interpretation and visualization tool that decomposes PRSs into genes/regions and single nucleotide polymorphism (SNP) contribution scores via Shapley additive explanations (SHAPs), which provide insights into specific genes and SNPs that significantly contribute to the PRS of an individual. 

In this best practice we use a co-design approach to help various stakeholders to embed ethics across the whole span of the design and implementation process of the XPRS system design and implementation. For that, we use Z-inspection® an ethically aligned Trustworthy AI co-design methodology.