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Summer Term 2024 Information
In the summer term 2024 we offer the lecture AIbDs II, the master seminar Case Studies of Ai implementation as well as the scientific project Applications of AI. For the seminars, please follow the faculty wide application process for scientific projects and master seminars. If your application is accepted, we will automatically invite you to the related e-learning course. For the AIbDS II lecture you need to enroll in the respective e-learning course (not just LSF!). Once the e-learning is updated for the summer term 2024, you will find the course accessible within e-learning platform.
Winter Term 2023/24 information
In the winter term 2023/24 we will offer in the Bachelor's program a seminar that deals with legal, ethical, and economic aspects of Artificial Intelligence and in the Master's program we will offer the lecture
AI-based Decision Support I as well as the Scientific Project: Applications of Artificial Intelligence. And as a new seminar, we will offer the master seminar Case Studies of AI implementation. As every term, we also have openings for master thesis topics.Application schedule:
- AIbDS I is available for enrollment now - just visit the e-learning site here.
- For the Scientific project, you have to use the central application process provided by the faculty and the dean of study affairs. You will be added to the e-learning course automatically by us on 15.09.2023. A mail will be sent out prior to the enrollment (the date of the mail depends on your priority I or II) to confirm you are in the seminar
- To apply for the master seminar Case Studies of AI implementation, please send an e-mail with your motivation and your grades to until 14.09.2023. After checking the requirements are met, you will be enrolled into the e-learning course.
- For the master's application, please refer to the info page linked above -the deadline is September 1st, 2023 (!!) - this will be an application via the ORBA master's application process
Article on AI adoption published in Electronic Markets
Contemporary decision support systems are increasingly relying on artificial intelligence technology such as machine learning algorithms to form intelligent systems. These systems have human-like decision capacity for selected applications based on a decision rationale which cannot be looked-up conveniently and constitutes a black box. As a consequence, acceptance by end-users remains somewhat hesitant. While lacking transparency has been said to hinder trust and enforce aversion towards these systems, studies that connect user trust to transparency and subsequently acceptance are scarce. In response, our research is concerned with the development of a theoretical model that explains end-user acceptance of intelligent systems. We utilize the unified theory of acceptance and use in information technology as well as explanation theory and related theories on initial trust and user trust in information systems. The proposed model is tested in an industrial maintenance workplace scenario using maintenance experts as participants to represent the user group. Results show that acceptance is performance-driven at first sight. However, transparency plays an important indirect role in regulating trust and the perception of performance.
The article is open access and available at: https://link.springer.com/article/10.1007/s12525-022-00593-5