Big Data Analytics & (Gen)AI:

Today, companies and organizations have access to vast and ever-growing amounts of data, such as from social media, the internet, databases, customer interactions, or human resources management; in fields like professional sports, extensive datasets are also generated from game and tracking data. Since much of this information is unstructured (e.g., images, videos, or text), automated analysis methods are required. In the field of Big Data Analytics & (Gen)AI, we therefore investigate the potential applications and benefits of AI methods, including generative AI, for analyzing such data, while Explainable AI (XAI) addresses the transparency of these methods. 

For example, in the following projects:

• Automation of incoming mail in the insurance industry
• Evaluation of player actions in football
• Use of humanoid robots in customer service
• Future skills and competencies
• GenAI in knowledge work
• Information extraction
• (Partial) automation of online customer service

Datenqualität:

Much of the ever-growing volume of data in companies is characterized by poor data quality. This results in significant economic losses. However, poor data quality is not only a major problem in business—in the age of “fake news”, the need for reliable information is also growing in politics and society. Therefore, quantitative methods are needed to measure, manage, and improve data quality.

This issue is particularly critical in the age of artificial intelligence (AI), as poor data quality directly leads to uncertainty. If uncertainties in AI predictions are ignored—for example, by replacing missing values with averages—this often results in overly confident and sometimes incorrect recommendations. For human decision-makers in human-AI teams, this poses significant risks: a false sense of security can lead to overreliance and poor decisions. To use AI responsibly and trustworthily, uncertainties arising from data quality defects must therefore be quantitatively captured, transparently presented, and disclosed.

This is being addressed in these research projects:

• Data Quality in the Automotive Industry
• Data Quality in User-Generated Content (DQNGI)
• Data Quality in User-Generated Content (DQUGC)
• Data Quality Measurement and Measures for Wikis and Knowledge Graphs (DQMM@Wiki)
• Measuring and Improving Data Quality in Unstructured Data (DQMM)
• Making uncertainties visible: Uncertainty-aware AI

Explainable AI (XAI)

Artificial intelligence (AI) is playing an increasingly important role in our daily lives—from chatbots and spam filters to fraud detection and marketing analytics in businesses. Despite their great potential, AI systems are under close scrutiny, particularly in areas involving critical decisions, such as financial management or lending. A key reason for this is their often limited transparency. Studies show that many Europeans view AI decisions with skepticism—even when these decisions demonstrably deliver better results than human experts. The research field of Explainable AI (XAI) addresses this very issue and develops methods to make the functioning and results of AI systems comprehensible to humans. This is being done, for example, in the following projects:

• Explainable AI in Controlling
• Guess the City
• Personal inflation calculator
• Skill Compass
• Review-based explanations for recommendations in e-commerce
• XAI Demonstrator
• XAI Studio
• XAI-as-a-Service (XAIaaS)
• XAI in Continuing Education (XPERT)
• XAI and Human-in-the-Loop (X-Loop)

Social Impact of Information Systems

Modern information systems not only create economic value but can also help address societal challenges. Our research on the social impact of information systems focuses on issues such as unemployment, skills shortages, integration, and strengthening democracy. Studies show, for example, that digital applications support the efforts of young job seekers, and that online peer groups (digital self-help groups) in particular make an important contribution in various contexts—such as unemployment, career guidance, or the integration of refugees. The main advantages of digital systems are flexibility in terms of time and location, as well as the possibility of anonymous and protected interaction. Artificial intelligence can also promote social innovation, for example through scalable and personalized counseling. We are conducting research on this in the following projects:

• Bias and fairness in AI
• Digital Peer Group Counselling (DIGIPEG)
• Explainable AI for Learning about Digital Disinformation (EKILED)
• Feasibility study: ‘App-based peer group counselling’
• Smart Integration

Mathias Klier and his team are the authors of numerous articles in books and academic journals such as the ACM Journal of Data and Information Quality, Decision Support Systems, Electronic Markets, the European Journal of Information Systems, the Journal of Management Information Systems, and Management Information Systems Quarterly. He has also presented the results of his work at international academic conferences such as the European Conference on Information Systems (ECIS), the International Conference on Information Systems (ICIS), and the International Conference on Business Informatics (WI).

List of publications