{"id":10095,"date":"2026-05-22T10:11:55","date_gmt":"2026-05-22T08:11:55","guid":{"rendered":"https:\/\/ki.winfo.uni-mainz.de\/?page_id=10095"},"modified":"2026-10-28T14:38:35","modified_gmt":"2026-10-28T13:38:35","slug":"en","status":"publish","type":"page","link":"https:\/\/ki.winfo.uni-mainz.de\/en\/","title":{"rendered":"Home"},"content":{"rendered":"\n<\/jgu-base-pageheader>\n \n<\/jgu-base-anchornavigation><\/div>\n\n
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Our research focuses on the development of machine learning methods in the context of responsible AI, particularly in biometric applications such as face recognition. Our goal is to develop learning algorithms that are technically robust, ethically fair, legally compliant, and under human control. We investigate how AI systems, and machine learning systems in particular, can be designed to be fairer, more privacy-friendly, more reliable, more explainable, and more secure, both in the biometric context and beyond.<\/p>\n\n\n\n

In our teaching, our goal is to equip students with both the fundamentals and advanced methods of responsible AI, and in particular, machine learning. In doing so, we aim to prepare our students to actively and successfully shape a data-driven future.<\/p>\n<\/div>\n\n\n\n

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AI systems offer great opportunities for the economy and individuals, but at the same time pose significant risks. These systems are never neutral, rather, they embody the values of the people who develop and use them. Responsible design of these systems is therefore essential to contributing to a digitally and socially sustainable society guided by shared values.<\/p>\n\n\n\n

Our research focuses on two interconnected areas: Responsible AI<\/strong> and biometric systems<\/strong>. Responsible AI aims to embed ethical principles, legal compliance, and public trust directly into the development and operation of AI models. Biometric systems enable the automatic verification of a person\u2019s identity based on behavioral or biological characteristics, such as fingerprints or facial recognition. These technologies are among the most accurate and influential AI systems for identity verification, access control, and personalized services in today\u2019s digital world.<\/p>\n\n\n\n

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In our teaching, our goal is to equip students with both the fundamentals and advanced methods of responsible AI, and in particular, machine learning. In the bachelor\u2019s program, the course \u201cMachine Learning and Artificial Intelligence\u201d<\/strong> introduces students to key learning methods and their practical application, laying a broad foundation. In the master\u2019s program, students build upon these foundations through seminars and project work, gaining specialization in research-oriented knowledge in courses such as \u201cAdvanced Learning Strategies for AI Systems<\/strong>,\u201d \u201cApplied AI Prototyping<\/strong>,\u201d and \u201cStrategy Mining<\/strong>.\u201d<\/p>\n\n\n\n

In the Machine Learning Lab I and II<\/strong> (bachelor) and Responsible AI Lab<\/strong> (master) courses, students work in small groups on research questions drawn from current research. Through weekly discussions with their supervisors, they develop new solutions, design and conduct experiments, and analyze the results. The topics addressed in these labs can be continued in a final thesis by mutual agreement. Of course, we also offer our own thesis topics.<\/p>\n\n\n\n

Our goal is to prepare our students to actively and successfully shape a data-driven future.<\/p>\n\n\n\n \n\n \n\n

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Winter Semester<\/strong><\/p>\n\n\n\n