Two New Articles Accepted at the European Conference on Information Systems (ECIS) 2024

(15.04.2024) Two papers of the cii research group have been accepted for publication at the 32nd European Conference on Information Systems (ECIS), which will take place in Paphos, Cyprus from 13th-19th June 2024.

Authors: Long Hoang Nguyen, Sebastian Lins, Maximilian Renner, Ali Sunyaev
Title: Unraveling the Nuances of AI Accountability: A Synthesis of Dimensions Across Disciplines
Abstract: The widespread diffusion of Artificial Intelligence (AI)-based systems offers many opportunities to contribute to the well-being of individuals and the advancement of economies and societies. This diffusion is, however, closely accompanied by public scandals causing harm to individuals, markets, or society,and leading to the increasing importance of accountability. AI accountability itself faces conceptual ambiguity, with research scattered across multiple disciplines. To address these issues, we review cur-rent research across multiple disciplines and identify key dimensions of accountability in the context of AI. We reveal six themes with 13 corresponding dimensions and additional accountability facilitators that future research can utilize to specify accountability scenarios in the context of AI-based systems.

Authors: Sascha Rank, Florian Leiser, Scott Thiebes, Ali Sunyaev
Title: Inter-Organizational Collaboration for Machine Learning: Motivating and Discouraging Factors in the Automotive Industry
Abstract: Many organizations are still far from harnessing the full potential of machine learning (ML). An auspicious solution to leverage the potential of ML is inter-organizational collaboration. In the context of ML, inter-organizational collaboration can benefit organizations enormously but also introduces some risks. Given these benefits and risks, deciding whether to participate in inter-organizational collaboration can be a delicate decision for organizations. We currently lack knowledge on the factors impacting organizations' decisions to engage in inter-organizational collaboration for ML. Using a ranking-type Delphi study, we identified 14 factors motivating (e.g., acquiring more extensive training data) and 11 factors discouraging (e.g., data protection concerns) inter-organizational collaboration for ML in the automotive industry and shed light on their relative importance. Our results lay the foundation for further research on ML that permeates organizational boundaries.