Online Peer Groups - a Design-Oriented Approach to Addressing the unemployment of People with Complex Barriers. - Annette Felgenhauer; Maximilian Förster; Katharina Kaufmann; Julia Klier; Mathias Klier (ECIS, 2019)

Evaluating Explainable Artifical Intelligence - What Users Really Appreciate. - Maximilian Förster; Mathias Klier; Kilian Kluge; Irina Sigler (ECIS, 2020)

Getting to the Heart of Groups - Analyzing Social Support and Sentiment in Online Peer Groups. - Patrick Bedué; Maximilian Förster; Mathias Klier; Kilian Zepf (ICIS, 2020)

Fostering Human Agency: A Process for the Design of User-Centric XAI Systems. - Maximilian Förster; Mathias Klier; Kilian Kluge; Irina Sigler (ICIS, 2020)

Same but Different - How Users Benefit in Online Peer Groups Depending on their User Role. - Maximilian Förster (ECIS, 2021)

Capturing Users' Reality: A Novel Approach to Generate Coherent Counterfactual Explanations. - Maximilian Förster; Philipp Hühn; Mathias Klier; Kilian Kluge (HICSS, 2021)

Leveraging the Power of Peer Groups for Refugee Integration. - Maximilian Förster; Julia Klier; Mathias Klier; Katharina Schäfer-Siebert; Irina Sigler (Bus. Inf. Syst. Eng., 2022)

Generierung regulärer Ausdrücke zur Prüfung von SQL-Statements. - Maximilian Förster; Patrick Binkert; Holger Rohland; Thiemo Leonhardt (DELFI Workshops, 2023)

Boosting Benefits, Offsetting Obstacles - the Impact of Explanations on AI Users' Task Performance. - Marie Christine Walter; Hanna Rebecca Broder; Maximilian Förster (Wirtschaftsinformatik, 2023)

Explainable artificial intelligence in information systems: A review of the status quo and future research directions. - Julia Brasse; Hanna Rebecca Broder; Maximilian Förster; Mathias Klier; Irina Sigler (Electron. Mark., 2023)

User-centric explainable AI: design and evaluation of an approach to generate coherent counterfactual explanations for structured data. - Maximilian Förster; Philipp Hühn; Mathias Klier; Kilian Kluge (J. Decis. Syst., 2023)

Wasn't Expecting that - Using Abnormality as a Key to Design a Novel User-Centric Explainable AI Method. - Tobias Jahn; Philipp Hühn; Maximilian Förster (DESRIST, 2024)

Exploring XAI Users' Needs: A Novel Approach to Personalize Explanations Using Contextual Bandits. - Philipp Schröppel; Maximilian Förster (ECIS, 2024)

Choose Wisely: Leveraging Explainable AI to Support Reflective Decision-Making. - Maximilian Förster; Philipp Schröppel; Chiara Schwenke; Lior Fink; Mathias Klier (ICIS, 2024)

Building AI Literacy with Experiential Learning - Insights from a Field Experiment in K-12 Education. - Maximilian Förster; Kirsten Pitz; Andrea Wrabel; Mathias Klier; Steffen Zimmermann (Wirtschaftsinformatik, 2024)

START Foundation: Coping with Bias and Fairness when Implementing and Using an AI System. - Chiara Schwenke; Julia Brasse; Maximilian Förster; Mathias Klier (Commun. Assoc. Inf. Syst., 2024)

Tell me more, tell me more: the impact of explanations on learning from feedback provided by Artificial Intelligence. - Maximilian Förster; Hanna Rebecca Broder; Marie C. Fahr; Mathias Klier; Lior Fink (Eur. J. Inf. Syst., 2025)

Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users. - Julian Rosenberger; Philipp Schröppel; Sven Kruschel; Mathias Kraus; Patrick Zschech; Maximilian Förster (CoRR, 2025)