Dagstuhl-Seminar 24342
Leveraging AI for Management Decision-Making
( 18. Aug – 21. Aug, 2024 )
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https://www.dagstuhl.de/24342
Organisatoren
- Stefan Feuerriegel (Ludwig-Maximilians-Universität München, DE)
- Foster Provost (New York University, US)
- Galit Shmueli (National Tsing Hua University - Hsinchu, TW)
Kontakt
- Andreas Dolzmann (für wissenschaftliche Fragen)
- Susanne Bach-Bernhard (für administrative Fragen)
Gemeinsame Dokumente
- Dagstuhl Materials Page (Use personal credentials as created in DOOR to log in)
Artificial intelligence (AI) is increasingly being used for management decision-making, both across a variety of industries (e.g. healthcare, banking, education, manufacturing, retail) and functions (e.g. marketing, operations). In marketing, for example, AI can predict business failures and thus act as an early warning system when service quality needs to be improved. In business process management, AI can help identify causes of poor quality and ultimately improve product quality.
Recent advances in AI research are promising for decision-making in companies and organizations. Driven by increases in data access, computing power, and algorithm advancements, modern AI algorithms are able to mimic human decision-making and judgment. This enables AI to complement and automate a variety of management decisions in business organizations. Overcoming existing hurdles in introducing AI into company practice requires an executive and interdisciplinary perspective.
The central topic of our Dagstuhl Seminar is the development, implementation, and evaluation of new AI technologies to support decision-making in management. A distinguishing feature is therefore innovative algorithms from the field of AI (e.g. explainable AI, generative AI, large language models, probabilistic ML, causal ML, etc.) that enable new insights in practice and beyond. This holds great potential for informing and improving decision-making.
Creative Commons BY 4.0
Stefan Feuerriegel, Foster Provost, and Galit Shmuel
Funding by the German Research Foundation (Grant: 530066172) and the Swiss National Science Foundation (SNSF) via Grant 186932 is acknowledged.
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- Kevin Bauer (Universität Mannheim, DE)
- Margrét Bjarnadóttir (University of Maryland - College Park, US) [dblp]
- Jessica M. Clark (University of Maryland - College Park, US) [dblp]
- Theodoros Evgeniou (INSEAD - Fontainebleau, FR)
- Carlos Fernández-Loria (HKUST - New Territories, HK) [dblp]
- Stefan Feuerriegel (Ludwig-Maximilians-Universität München, DE) [dblp]
- Sebastian Gabel (Erasmus University - Rotterdam, NL) [dblp]
- Travis Greene (Copenhagen Business School, DK) [dblp]
- Jungpil Hahn (National University of Singapore, SG) [dblp]
- Christian Janiesch (TU Dortmund, DE) [dblp]
- Enric Junqué de Fortuny (IESE Business School - Barcelona, ES) [dblp]
- Nadja Klein (KIT - Karlsruher Institut für Technologie, DE) [dblp]
- Mathias Kraus (Universität Erlangen-Nürnberg, DE) [dblp]
- Niklas Kühl (Universität Bayreuth, DE) [dblp]
- David Martens (University of Antwerp, BE) [dblp]
- Claudia Perlich (Two Sigma Investments LP - New York, US) [dblp]
- Joel Persson (Spotify - London, GB) [dblp]
- Foster Provost (New York University, US) [dblp]
- Galit Shmueli (National Tsing Hua University - Hsinchu, TW) [dblp]
- Sriram Somanchi (University of Notre Dame, US) [dblp]
- Wei Sun (IBM TJ Watson Research Center - Yorktown Heights, US) [dblp]
- Wouter Verbeke (KU Leuven, BE) [dblp]
- Michael Vössing (KIT - Karlsruher Institut für Technologie, DE) [dblp]
- Alona Zharova (HU Berlin, DE) [dblp]
- Patrick Zschech (Universität Leipzig, DE) [dblp]
Klassifikation
- Artificial Intelligence
- Machine Learning
Schlagworte
- Management
- Business
- Decision-making
- Applications
- Marketing
