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Ethical and Trustworthy AI in Finance

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Hosted By
Anita P. and Martin C.
Ethical and Trustworthy AI in Finance

Details

During this interactive workshop, you and a multi-disciplinary expert panel will reflect on how to implement ethical and trustworthy AI on a real use case in finance (e.g. credit card approval, loan approval).
The objective is to draft practical guidelines on how to implement ethical and trustworthy AI for a specific use case in finance.

Organizers:

  • Martin Canter: Head of Ethical AI at Omina Technologies
  • Anita Prinzie: Product and Marketing Manager at Omina Technologies

Confirmed workshop panel members:

  • Prof. dr. Bart Baesens, expert in credit scoring: Professor of Big Data & Analytics at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics, credit risk modeling, fraud detection, and marketing analytics. (credit scoring and AI expert).

*Brahim Benichou, legal expert: Legal researcher at the Knowledge Centre for Data and Society with special interest in AI, Data Protection and Tech. He co-founded the start-up My Privacy Specialist, a legal solution that helps SME's to implement the GDPR in a cost-effective way and tailored to the specific needs of SME's.

*Prof. dr. Karolien Poels, expert in persuasive communication: professor of Strategic Communication and Persuasive Technologies at the Department of Communication Studies, University of Antwerp, Belgium and member of the research group MIOS (Media & ICT in Organizations & Society).

*Prof. dr. Lode Lauwaert, expert in ethics: postdoctoral researcher and lecturer in the Institute of Philosophy at the University of Leuven. He has been a visiting scholar at Boston College, Stellenbosch University and the University of Vienna. His research focuses on ethics in technology/responsible AI, the use of (semi)-autonomous AI on a civilian level and military level. He teaches, among others things, ethics in technology.

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Ethical and Trustworthy AI for Business
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