Visiting Scholar Presentation: Dr. Sven Weinzierl

Friday, Nov. 15, 1:30-2:30 p.m.

BN-4000

Please join us for a presentation by visiting scholar Dr. Sven Weinzierl on Friday, November 15 from 1:30 – 2:30 pm in BN4000. Dr. Weinzierl, a visiting scholar in the Faculty of Business Administration, will present his paper “How risky is my AI system? A method for transparent classification of AI System descriptions.”

Abstract: Risk-based artificial intelligence (AI) regulations define risk categories for AI-enabled systems. The operators of such systems must determine the risk category applicable to their AI systems. This requires detailed knowledge of the classification rules defined in the regulations. Only a few supporting tools have been developed to facilitate the task of risk classification. This paper presents a novel method that describes all the necessary steps to develop such a tool. To demonstrate and evaluate the method, it is instantiated for the European Union’s AI Act. The evaluation shows i) that the classification model achieves promising performance in predicting the risk categories for AI systems, ii) that users can effectively use the web application to carry out a risk classification, and iii) that users find SHAP text plots integrated into the web application helpful for understanding the reasons of a classification prediction.

Bio: Sven Weinzierl is postdoctoral researcher at the Chair of Digital Industrial Service Systems at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany. In 2022, he received a Ph.D. degree in Information Systems from the FAU for his work on deep learning for decision support in process monitoring and analysis. His research interests focus on data-driven decision support in organizations. This includes the design and application of innovative machine learning and deep learning solutions, with a particular focus on various problems in information systems research and operations research. In these areas, he has published more than 30 articles, among others in the journals Health Care Management Science, European Journal of Operational Research, Decision Support Systems, and Business & Information Systems Engineering. He has participated in several research projects funded by the German Federal Ministry of Education and Research and industry. He is a fellow of the Schöller Research Center.

Please RSVP by to mgulliver@mun.ca by November 14.

Presented by Faculty of Business Administration

Event Listing 2024-11-15 13:30:00 2024-11-15 14:30:00 America/St_Johns Visiting Scholar Presentation: Dr. Sven Weinzierl Please join us for a presentation by visiting scholar Dr. Sven Weinzierl on Friday, November 15 from 1:30 – 2:30 pm in BN4000. Dr. Weinzierl, a visiting scholar in the Faculty of Business Administration, will present his paper “How risky is my AI system? A method for transparent classification of AI System descriptions.” Abstract: Risk-based artificial intelligence (AI) regulations define risk categories for AI-enabled systems. The operators of such systems must determine the risk category applicable to their AI systems. This requires detailed knowledge of the classification rules defined in the regulations. Only a few supporting tools have been developed to facilitate the task of risk classification. This paper presents a novel method that describes all the necessary steps to develop such a tool. To demonstrate and evaluate the method, it is instantiated for the European Union’s AI Act. The evaluation shows i) that the classification model achieves promising performance in predicting the risk categories for AI systems, ii) that users can effectively use the web application to carry out a risk classification, and iii) that users find SHAP text plots integrated into the web application helpful for understanding the reasons of a classification prediction. Bio: Sven Weinzierl is postdoctoral researcher at the Chair of Digital Industrial Service Systems at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany. In 2022, he received a Ph.D. degree in Information Systems from the FAU for his work on deep learning for decision support in process monitoring and analysis. His research interests focus on data-driven decision support in organizations. This includes the design and application of innovative machine learning and deep learning solutions, with a particular focus on various problems in information systems research and operations research. In these areas, he has published more than 30 articles, among others in the journals Health Care Management Science, European Journal of Operational Research, Decision Support Systems, and Business & Information Systems Engineering. He has participated in several research projects funded by the German Federal Ministry of Education and Research and industry. He is a fellow of the Schöller Research Center. Please RSVP by to mgulliver@mun.ca by November 14. BN-4000 Faculty of Business Administration