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ItemAI Agency Risks and Their Mitigation Through Business Process Management: A Conceptual Framework( 2019-01-08)After over 60 years of research and development, AI has made its way into mainstream business operations. Continuous advances in the fields of machine learning, knowledge representation, and logical reasoning are expected to result in higher autonomy of AI-enabled systems such as Distributed AI (DAI) agents that can think and act. The increased agency of the AI systems is expected to result in agency risks and the need for mitigating such risks through AI governance. In this paper, we build on agency theory and identify factors that increase the risk of an agency problem between a principal (a human or an organization) and an AI agent and propose a framework for AI agency problem analysis. The framework is illustrated through AI use cases and industry examples. Implications for AI governance research and practice are discussed.
ItemMultivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications( 2019-01-08)Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques, that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems. The research was conducted in collaboration with a European automotive company, using two months of live application data. We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes.
ItemUnderstanding the Changing Role of the Management Accountant in the Age of Industry 4.0 in Germany( 2019-01-08)Currently, business processes are undergoing a transformation through digitalization. In the course of this development, “Industry 4.0” also has an impact on management accounting and IT systems. By promising technical development and improvement of management accounting processes, the conventional roles are challenged and the introduction of new occupational fields is prognosticated. However, in theory and practice, the impact of industry 4.0 (I4.0) on management accounting has not been considered adequately so far. Against this background, the primary goal of this study is to investigate the implications of I4.0 on management accounting by means of a triangulation approach. The results of expert interviews, a literature review and an analysis of job advertisements provide an overview on the development of competencies of Management Accountants (MA) in Germany. The results have highlighted a closer cooperation of MA and Data Scientists. Additionally, there will be a shift from traditional analysis towards statistical analysis methods.