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- Fachbereich Wirtschaftswissenschaften (1138) (remove)
AI-based systems are nearing ubiquity not only in everyday low-stakes activities but also in medical procedures. To protect patients and physicians alike, explainability requirements have been proposed for the operation of AI-based decision support systems (AI-DSS), which adds hurdles to the productive use of AI in clinical contexts. This raises two questions: Who decides these requirements? And how should access to AI-DSS be provided to communities that reject these standards (particularly when such communities are expert-scarce)? This chapter investigates a dilemma that emerges from the implementation of global AI governance. While rejecting global AI governance limits the ability to help communities in need, global AI governance risks undermining and subjecting health-insecure communities to the force of the neo-colonial world order. For this, this chapter first surveys the current landscape of AI governance and introduces the approach of relational egalitarianism as key to (global health) justice. To discuss the two horns of the referred dilemma, the core power imbalances faced by health-insecure collectives (HICs) are examined. The chapter argues that only strong demands of a dual strategy towards health-secure collectives can both remedy the immediate needs of HICs and enable them to become healthcare independent.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
Allgemeines Steuerrecht
(2015)
Anmerkung zu BGH VII ZR 135/00 (Widerklage gegen einen am Rechtsstreit nicht beteiligten Dritten)
(2002)
Anmerkung zu EuGH RS C-208/00 (Niederlassungsfreiheit und Internationales Gesellschaftsrecht)
(2003)
Anmerkung zu EuGH, Urt. v. 1.3.2011, Rs. C-236/09 Association belge des Consomma-teurs Test-Achats
(2011)
Die Entscheidung in der Rechtssache Bohez /Wiertz bot dem EuGH Gelegenheit, zur Abgrenzung der Anwendungsbereiche von Brüssel I-(jetzt: Brüssel Ia-) und Brüssel IIa-VO Stellung zu nehmen. Den Ausgangspunkt bildete dabei ein familienrechtlicher Sachverhalt, nämlich die zwangsweise Durchsetzung des Umgangsrechts eines Vaters im Hinblick auf seine beiden Kinder. Auf den ersten Blick lag daher eine Anwendung der auf Verfahren betreffend die elterliche Verantwortung bezogenen Brüssel IIa-VO nahe. Andererseits schien auch eine Argumentation denkbar, wonach es sich bei dem zu vollstreckenden Anspruch auf Zahlung des Zwangsgeldes um eine Geldforderung handele, deren Vollstreckung nach der Brüssel I-VO zu erfolgen habe.
Was vordergründig die Ermittlung des einschlägigen EU-Rechtsaktes betraf, erwies sich bei genauerer Betrachtung als Bestimmung der dogmatischen Rechtsnatur des Zwangsgeldes.
Anwendung des Haustürgeschäftewiderrufsgesetzes unter Angehörigen (BGH, Urteil vom 17.09.1996)
(1997)
Software development projects often fail because of insufficient code quality. It is now well documented that the task of testing software, for example, is perceived as uninteresting and rather boring, leading to poor software quality and major challenges to software development companies. One promising approach to increase the motivation for considering software quality is the use of gamification. Initial research works already investigated the effects of gamification on software developers and come to promising. Nevertheless, a lack of results from field experiments exists, which motivates the chapter at hand. By conducting a gamification experiment with five student software projects and by interviewing the project members, the chapter provides insights into the changing programming behavior of information systems students when confronted with a leaderboard. The results reveal a motivational effect as well as a reduction of code smells.
Process mining gets more and more attention even outside large enterprises and can be a major benefit for small and medium sized enterprises (SMEs) to gain competitive advantages. Applying process mining is challenging, particularly for SMEs because they have less resources and process maturity. So far, IS researchers analyzed process mining challenges with a focus on larger companies. This paper investigates the application of process mining by means of a case study and sheds light into the particular challenges of an IT SME. The results reveal 13 SME process mining challenges and seven guidelines to address them. In this way, the paper contributes to the understanding of process mining application in SME and shows similarities and differences to larger companies.