http://dl.gi.de/handle/20.500.12116/39308 2026-07-22T20:33:42Z http://dl.gi.de/handle/20.500.12116/39318 The Cost of Fairness in AI: Evidence from E-Commerce Zahn, Moritz; Feuerriegel, Stefan; Kuehl, Niklas Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness�? have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness. 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39317 Interview with Peter Mertens and Wolfgang König: “From Reasonable Automation to (Sustainable) Autonomous Systems"? Beck, Roman; Dibbern, Jens; Wiener, Martin 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39316 Concepts for Modeling Smart Cities Bastidas, Viviana; Reychav, Iris; Ofir, Alon; Bezbradica, Marija; Helfert, Markus The rapid increase and adoption of new Information Technologies (IT) in Smart Cities make the provision of public services more efficient. However, various municipalities and cities deal with challenges to transform and digitize city services. Smart Cities have a high degree of complexity where offered city services must respond to the concerns and goals of multiple stakeholders. These city services must also involve diverse data sources, multi-domain applications, and heterogeneous systems and technologies. Enterprise Architecture (EA) is an instrument to deal with complexity in both private and public organizations. The paper defines the concepts for modeling Smart Cities in ArchiMate, guided by a design-oriented research approach. Particularly, the focus of this paper is on the concepts for modeling city services and underlying information systems which are added to the EA metamodel. The metamodel is demonstrated in a real-world case and validated by Smart City domain experts. The findings suggest that these concepts are essential to achieve the Smart City strategy (e.g., city goals and objectives), as well as to meet the needs of different city stakeholders. Furthermore, an extension mechanism allows addressing the alignment of business and IT in complex environments such as Smart Cities, by adjusting EA metamodels and notations. This can help cities to design, visualize, and communicate architecture decisions when managing the transformation and digitalization of public services. 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39311 When Self-Humanization Leads to Algorithm Aversion Heßler, Pascal Oliver; Pfeiffer, Jella; Hafenbrädl, Sebastian Decision support systems are increasingly being adopted by various digital platforms. However, prior research has shown that certain contexts can induce algorithm aversion, leading people to reject their decision support. This paper investigates how and why the context in which users are making decisions (for-profit versus prosocial microlending decisions) affects their degree of algorithm aversion and ultimately their preference for more human-like (versus computer-like) decision support systems. The study proposes that contexts vary in their affordances for self-humanization. Specifically, people perceive prosocial decisions as more relevant to self-humanization than for-profit contexts, and, in consequence, they ascribe more importance to empathy and autonomy while making decisions in prosocial contexts. This increased importance of empathy and autonomy leads to a higher degree of algorithm aversion. At the same time, it also leads to a stronger preference for human-like decision support, which could therefore serve as a remedy for an algorithm aversion induced by the need for self-humanization. The results from an online experiment support the theorizing. The paper discusses both theoretical and design implications, especially for the potential of anthropomorphized conversational agents on platforms for prosocial decision-making. 2022-01-01T00:00:00Z