TY - CONF AB - 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. AU - Kucklick, Jan-Peter ID - 29539 KW - Explainable Artificial Intelligence KW - XAI KW - Interpretability KW - Decision Support Systems KW - Taxonomy T2 - Wirtschaftsinformatik 2022 Proceedings TI - Towards a model- and data-focused taxonomy of XAI systems ER - TY - CONF AB - When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system which we develop in joint cooperation with a disaster management authority and a software development company. AU - Rauchecker, Gerhard AU - Schryen, Guido ID - 5675 KW - Coordination of spontaneous volunteers KW - volunteer coordination system KW - decision support KW - scheduling optimization model KW - linear programming T2 - Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management TI - Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief ER - TY - JOUR AB - Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks. AU - Scholz, Michael AU - Dorner, Verena AU - Schryen, Guido AU - Benlian, Alexander ID - 5671 IS - 1 JF - European Journal of Operational Research KW - E-Commerce KW - Recommender System KW - Attribute Weights KW - Configuration System KW - Decision Support TI - A configuration-based recommender system for supporting e-commerce decisions VL - 259 ER - TY - CONF AB - Many academic disciplines - including information systems, computer science, and operations management - face scheduling problems as important decision making tasks. Since many scheduling problems are NP-hard in the strong sense, there is a need for developing solution heuristics. For scheduling problems with setup times on unrelated parallel machines, there is limited research on solution methods and to the best of our knowledge, parallel computer architectures have not yet been taken advantage of. We address this gap by proposing and implementing a new solution heuristic and by testing different parallelization strategies. In our computational experiments, we show that our heuristic calculates near-optimal solutions even for large instances and that computing time can be reduced substantially by our parallelization approach. AU - Rauchecker, Gerhard AU - Schryen, Guido ID - 5678 KW - scheduling KW - decision support KW - heuristic KW - high performance computing KW - parallel algorithms T2 - Australasian Conference on Information Systems TI - High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic ER - TY - CONF AB - In double-sided markets for computing resources an optimal allocation schedule among job offers and requests subject to relevant capacity constraints can be determined. With increasing storage demands and emerging storage services the question how to schedule storage jobs becomes more and more interesting. Since such scheduling problems are often in the class NP-complete an exact computation is not feasible in practice. On the other hand an approximation to the optimal solution can easily be found by means of using heuristics. The problem with this attempt is that the suggested solution may not be exactly optimal and is thus less satisfying. Considering the two above mentioned solution approaches one can clearly find a trade-off between the optimality of the solution and the efficiency to get to a solution at all. This work proposes to apply and combine heuristics in optimization to gain from both of their benefits while reducing the problematic aspects. Following this method it is assumed to get closer to the optimal solution in a shorter time compared to a full optimization. AU - Finkbeiner, Josef AU - Bodenstein, Christian AU - Schryen, Guido AU - Neumann, Dirk ID - 5685 KW - Decision Support System KW - Algorithms KW - Optimization KW - Market Engineering T2 - 18th European Conference on Information Systems (ECIS 2010) TI - Applying heuristic methods for job scheduling in storage markets ER -