@inproceedings{67286,
  abstract     = {{The effects of decisions in strategic product planning are usually far-reaching, but at the same time difficult to assess. Simulations help to make these effects more tangible. Until now, the use of simulation in strategic product planning has been limited to behavioral and innovation diffusion studies. This paper presents a process model for widening business models or planning upcoming product generations. It supports integrating the simulation of technical systems into decisionmaking processes to select the most suitable product for adaptation to new application scenarios. To this end, the characteristics of such new application scenarios are applied to existing product models in simulation, and the system behavior is evaluated based on key figures. This supports the comparison of different products and the selection of the product alternative requiring the least amount of modification. The process model enables decision-makers to systematically integrate simulation into the decision-making process and thus make more data-based decisions. }},
  author       = {{Gräßler, Iris and Özcan, Deniz and Döhner, Niklas}},
  booktitle    = {{24th INTERNATIONAL INDUSTRIAL SIMULATION CONFERENCE }},
  editor       = {{Gonzalez, Jose David Nunez and Romay, Manuel Grana}},
  isbn         = {{978-9-492859-40-2}},
  keywords     = {{Decision-making, Decision support systems, Mechanical engineering, System analysis, System engineering}},
  location     = {{San Sebastian, Spain}},
  pages        = {{65--72}},
  title        = {{{Leveraging System Simulation for Decision  Support in Strategic Product Planning}}},
  volume       = {{24}},
  year         = {{2026}},
}

@article{67595,
  abstract     = {{In the aftermath of natural disasters, the timely coordination of spon-
taneous volunteers is crucial for minimising harm and preventing casualties. How-
ever, research into decision support methodologies and systems for coordinating
volunteers is scarce. Building on previous work in this area, we propose combin-
ing heuristic and exact procedures. Based on real-world data from a flood scenario
in Halle (Saale, Germany) in 2018, we propose and computationally evaluate
different combinations. Our results show that using a combination of a heuristic
and a MILP solver allows for the efficient computation of effective solutions for
up to 500 volunteers. Our results also show that the most suitable mode depends
on the size of the problem in terms of the number of volunteers.}},
  author       = {{Goecke, Manuel and Gnade, Judith and Schryen, Guido}},
  journal      = {{International Journal of Operational Research,}},
  keywords     = {{Disaster Management, Volunteer Coordination, Decision Support Systems, MILP}},
  title        = {{{Coordinating Spontaneous Volunteers in the Aftermath of Natural Disasters: Development and Computational Evaluation of Heuristic Methods}}},
  year         = {{2026}},
}

@misc{56282,
  abstract     = {{Algorithmic bias has long been recognized as a key problem affecting decision-making processes that integrate artificial intelligence (AI) technologies. The increased use of AI in making military decisions relevant to the use of force has sustained such questions about biases in these technologies and in how human users programme with and rely on data based on hierarchized socio-cultural norms, knowledges, and modes of attention.

In this post, Dr Ingvild Bode, Professor at the Center for War Studies, University of Southern Denmark, and Ishmael Bhila, PhD researcher at the “Meaningful Human Control: Between Regulation and Reflexion” project, Paderborn University, unpack the problem of algorithmic bias with reference to AI-based decision support systems (AI DSS). They examine three categories of algorithmic bias – preexisting bias, technical bias, and emergent bias – across four lifecycle stages of an AI DSS, concluding that stakeholders in the ongoing discussion about AI in the military domain should consider the impact of algorithmic bias on AI DSS more seriously.}},
  author       = {{Bhila, Ishmael and Bode, Ingvild}},
  keywords     = {{Algorithmic Bias, AI, Decision Support Systems, Autonomous Weapons Systems}},
  publisher    = {{ICRC Humanitarian Law & Policy Blog}},
  title        = {{{The problem of algorithmic bias in AI-based military decision support systems}}},
  year         = {{2024}},
}

@inproceedings{29539,
  abstract     = {{Explainable Artificial Intelligence (XAI) is currently an important topic for the application of Machine Learning (ML) in high-stakes decision scenarios. Related research focuses on evaluating ML algorithms in terms of interpretability. However, providing a human understandable explanation of an intelligent system does not only relate to the used ML algorithm. The data and features used also have a considerable impact on interpretability. In this paper, we develop a taxonomy for describing XAI systems based on aspects about the algorithm and data. The proposed taxonomy gives researchers and practitioners opportunities to describe and evaluate current XAI systems with respect to interpretability and guides the future development of this class of systems.}},
  author       = {{Kucklick, Jan-Peter}},
  booktitle    = {{Wirtschaftsinformatik 2022 Proceedings}},
  keywords     = {{Explainable Artificial Intelligence, XAI, Interpretability, Decision Support Systems, Taxonomy}},
  location     = {{Nürnberg (online)}},
  title        = {{{Towards a model- and data-focused taxonomy of XAI systems}}},
  year         = {{2022}},
}