Lessons Learned: Co-design of Trustworthy AI. Best Practice. By Helga Brogger, President of the Norwegian Society of Radiology

Mission: …Aid the development of designs with reduced end-user vulnerability…

-“…Socio-technical scenarios can be used to broaden stakeholders’ understanding of one’s own role in the technology, as well as awareness of stakeholders’ interdependence…”

– “…Recurrent, open-minded, and interdisciplinary discussions involving different perspectives of the broad problem definition….”

– “…The early involvement of an interdisciplinary panel of experts broadened the horizon of AI designers which are usually focused on the problem definition from a data and application perspective…”

– “…Consider the aim of the future AI system as a claim that needs to be validated before the AI system is deployed..”

-“…Involve patients at every stage of the design process … it is particularly important to ensure that the views, needs, and preferences of vulnerable and disadvantaged patient groups are taken into account to avoid exacerbating existing inequalities…”

Thank you, Roberto V. Zicari and the rest of the team for these insights!

— Helga Brogger, President of the Norwegian Society of Radiology

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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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Our paper ” On Assessing Trustworthy AI in Healthcare Best Practice for Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls.” has been accepted for publication in Frontiers in Human Dynamics

The legislative proposal for AI by the European Commission has been published today.

The highly anticipated legislative proposal for AI by the European Commission has been published today.

Read the EU Regulatory Proposal on AI:

https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-european-approach-artificial-intelligence

EU Press Release

Press release 21 April 2021 Brussels

Europe fit for the Digital Age: Commission proposes new rules and actions for excellence and trust in Artificial Intelligence

Kick off Meeting (April 15, 2021) 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

On April 15, 2021 we had a real great kick off meeting for this use case:

Assessing Trustworthy AI. Best Practice: Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients.

71 experts from all over the world attended.

Worldwide, the saturation of healthcare facilities, due to the high contagiousness of Sars-Cov-2 virus and the significant rate of respiratory complications is indeed one among the most critical aspects of the ongoing COVID-19 pandemic
The team of Alberto Signoroni and colleagues implemented an end-to-end deep learning architecture, designed for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients.

We will work with Alberto Signoroni and his team and apply our Z-inspection® process to assess the ethical, technical and legal implications of using Deep Learning in this context.

For more information: http://z-inspection.org/best-practice-deep-learning-for-predicting-a-multi-regional-score-conveying-the-degree-of-lung-compromise-in-covid-19-patients/

This AI detects cardiac arrests during emergency calls

Jointly with the Emergency Medical Services Copenhagen, we completed the first part of our trustworthy AI assessment.
A ML sytem is currently used as a supportive tool to recognize cardiac arrest in 112 emergency calls.
A team of multidisciplinary experts used Z-Inspection® and
identified  ethical,technical and legal issues in using such AI system.
This confirms some of the ethical concern raised by Kay Firth-Butterfield, back in June 2018….

This is another example of the need to test and verify algorithms,says Kay Firth-Butterfieldhead of Artificial Intelligence and Machine Learning at the World Economic Forum.

“We all want to believe that AI will ‘wave its magic wand’ and help us do better and this sounds as if it is a way of getting AI to do something extremely valuable.
“But,” Firth-Butterfield added, “it still needs to meet the requirements of transparency and accountability and protection of patient privacy. As it is in the EU, it will be caught by GDPR, so it is probably not a problem.” However, the technology raises the fraught issue of accountability, as Firth-Butterfield explains. Who is liable if the machine gets it wrong? the AI manufacturer, the human being advised by it, the centre using it? This is a much debated question within AI which we need to solve urgently: when do we accept that if the AI is wrong it doesn’t matter because it is significantly better than humans. Does it need to be a 100% better than us or just a little better? At what point is the use, or not using this technology negligent?

Source: https://www.weforum.org/agenda/2018/06/this-ai-detects-cardiac-arrests-during-emergency-calls/

Image: CPR

The full report is submitted for publication. Contact me if you are interested to know more. RVZ

Resources:

Article World Economic Forum, 06 Jun 2018.

Download the Z-Inspection® Process

“Z-Inspection®: A Process to Assess Ethical AI”
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, 2021
Print ISSN: 2637-6415
Online ISSN: 2637-6415
Digital Object Identifier: 10.1109/TTS.2021.3066209
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The Z-Inspection® Process is available for Download!

The Z-Inspection® Process is available for Download! “Z-Inspection®: A Process to Assess Ethical AI”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 […]

Presentation on Z-Inspection® at the [AI4EU] Trustworthy AI workshop. November 13, 2020

Roberto V. Zicari did a 30 minutes presentation at the [AI4EU] Trustworthy AI workshop  on our research on Z-Inspection® , a process to assess Trustworthy AI.

YouTube: Link to when the presentation starts