Authors:
Miriam Elia
and
Bernhard Bauer
Affiliation:
Faculty of Applied Computer Science, University of Augsburg, Germany
Keyword(s):
Certifiable AI, Quality Management, Machine Learning, Healthcare, Metrics, Deep Learning, Performance Evaluation, Algorithm Auditing.
Abstract:
As of now, intelligent technologies experience a rapid growth. For a reliable adoption of those new and powerful systems into day-to-day life, especially with respect to high-risk settings such as medicine, technical means to realize legal requirements correctly, are indispensible. Our proposed methodology comprises an approach to translate such partly more abstract concepts into concrete instructions - it is based on Quality Gates along the intelligent system’s complete life cycle, which are composed of use-case adapted Criteria that need to be addressed with respect to certification. Also, the underlying philosophy regarding stakeholder inclusion, domain embedding and risk analysis is illustrated. In the present paper, the Quality Gate Metrics is outlined for the application of machine learning performance metrics focused on binary classification.