Logo des Repositoriums

BISE 64(3) - June 2022

https://dl.gi.de/handle/20.500.12116/39308
Auflistung nach:

Auflistung BISE 64(3) - June 2022 nach Schlagwort "Artificial intelligence"

1 - 2 von 2
  • Zeitschriftenartikel
    Radiologists’ Usage of Diagnostic AI Systems
    (Business & Information Systems Engineering: Vol. 64, No. 3, 2021) Jussupow, Ekaterina; Spohrer, Kai; Heinzl, Armin
    While diagnostic AI systems are implemented in medical practice, it is still unclear how physicians embed them in diagnostic decision making. This study examines how radiologists come to use diagnostic AI systems in different ways and what role AI assessments play in this process if they confirm or disconfirm radiologists’ own judgment. The study draws on rich qualitative data from a revelatory case study of an AI system for stroke diagnosis at a University Hospital to elaborate how three sensemaking processes revolve around confirming and disconfirming AI assessments. Through context-specific sensedemanding, sensegiving, and sensebreaking, radiologists develop distinct usage patterns of AI systems. The study reveals that diagnostic self-efficacy influences which of the three sensemaking processes radiologists engage in. In deriving six propositions, the account of sensemaking and usage of diagnostic AI systems in medical practice paves the way for future research.
  • Zeitschriftenartikel
    The Cost of Fairness in AI: Evidence from E-Commerce
    (Business & Information Systems Engineering: Vol. 64, No. 3, 2022) 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.