Publications
Towards Energy-Efficient Large-Scale Artificial Intelligence for Sustainable Data Centers
Dokic, D., Stein, H., Janzen, S., Maass, W.
INFORMATIK 2023. Gesellschaft für Informatik, Bonn. Umweltinformatik zwischen Nachhaltigkeit und Wandel (UINW). Berlin. 26.-30. September 2023
This paper addresses the challenges of energy-efficient AI in sustainable data centers.
Valuation of Personal Data in the Age of Data Ownership (Paper-a-thon)
Stein, H., Reich, R. H., Visinescu, L.
In: International Conference on Information Systems (ICIS) 2022. International Conference on Information Systems (ICIS-2022), December 9-14, Copenhagen, Denmark, 12/2022
Explores methods for valuing personal data amidst the growing focus on data ownership.
A Stress-Based Smart Retail Service in Shopping Environments: An Adoption Study
Öksüz-Köster, N.
In: Proceedings NeuroIS Retreat 2022. NeuroIS Retreat, June 14-16, Vienna, Austria
Investigates the adoption of a stress-based smart retail service in shopping environments.
AI Explainability: Embedding Conceptual Models
Maass, W., Castellanos, A., Tremblay, M., Lukyanenko, R., & Storey, V.C.
ICIS 2022 Proceedings. 12
Introduces methods to embed conceptual models into AI for improved explainability.
AI meets Design Science – Towards Design Methods for AI Systems Development
Janzen, S., Stein, H., Öksüz-Köster, N., Maass, W.
In: International Conference on Information Systems (ICIS) 2022 TREOs. International Conference on Information Systems (ICIS-2022), December 9-14, Copenhagen, Denmark
Proposes design methods for AI systems development, bridging AI and design science.
AI-based Approach for Improving the Detection of Blood Doping in Sports
Rahman, M.R., Bejder, J., Bonne, T.C., Andersen, A.B., Huertas, J.R., Aikin, R., Nordsborg, N.B., & Maass, W.
arXiv:2203.00001
Presents an AI-based approach to enhance the detection of blood doping in sports.
Approaches for Automated Data Quality Analysis: Syntactic and Semantic Assessment
Ahiagble, A.P., Stein, H.
Annual conference of the Society for Computer Science (INFORMATIK-2022), September 26-30, Hamburg, Germany
Discusses automated methods for syntactic and semantic data quality analysis.
Bewertung von Unternehmensdatenbeständen: Wege zur Wertermittlung des wertvollsten immateriellen Vermögensgegenstandes
Stein, H., Groen in’t Woud, F., Holuch, M., Mulryan, D., Froese, T., Holst, L.
In: Wie aus Daten Wert entsteht: Band 1: Datenwirtschaft und Datentechnologie. Springer, 2022
Discusses methods for valuing corporate data assets in the context of intangible assets.
ConceptSuperimposition: Using Conceptual Modeling Method for Explainable AI
Maass, W., Castellanos, A., Tremblay, M.C., Lukyanenko, R., & Storey, V.C.
In AAAI Spring Symposium: MAKE (pp. 1-6)
Explores the use of conceptual modeling methods for enhancing explainable AI.
Contract-based Data-sharing for AI-based Decision Making on the Web
Maass, W.
55th Hawaii International Conference on System Sciences (HICSS-2022), January 4-7, Hawaii, Hawaii, United States. Springer
Proposes a contract-based framework for data-sharing to facilitate AI-driven decision-making.
Data Analytics for Uncovering Fraudulent Behaviour in Elite Sports
Rahman, M.R., Piper, T., Geyer, H., Equey, T., Baume, N., Aikin, R., Maass, W.
43rd International Conference on Information Systems (ICIS 22), Copenhagen, Denmark
Applies data analytics to uncover fraudulent behaviors in elite sports.
Data Sharing in German Food Industry – Empirical Insights
Stein, H., Rix, C., Effertz, A., Bergau, S., Maass, W.
In Proceedings of the Americas Conference on Information Systems (AMCIS)
Provides empirical insights into data sharing practices in the German food industry.
Detection of Erythropoietin in Blood to Uncover Doping in Sports using Machine Learning
Rahman, M.R., Bejder, J., Bonne, T.C., Andersen, A.B., Huertas, J.R., Aikin, R., Nordsborg, N.B., Maass, W.
In Proceedings of the IEEE International Conference on Digital Health (ICDH), pp. 193-201
Explores machine learning methods to detect erythropoietin in blood for uncovering doping in sports.
Explainable AI
Storey, V.C., Lukyanenko, R., Maass, W., & Parsons, J.
Communications of the ACM, 65(4), 27-29
Discusses the principles and challenges of explainable AI in various domains.
Explainable AI: Opening the Black Box or Pandora's Box?
Storey, V., Lukyanenko, R., Maass, W., Parsons, J.
Communications of the ACM (CACM)
Examines the challenges and opportunities in implementing explainable AI.