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Nowadays, digitalization has an immense impact on the landscape of jobs. This technological revolution creates new industries and professions, promises greater efficiency and improves the quality of working life. However, emerging technologies such as robotics and artificial intelligence (AI) are reducing human intervention, thus advancing automation and eliminating thousands of jobs and whole occupational images. To prepare employees for the changing demands of work, adequate and timely training of the workforce and real-time support of workers in new positions is necessary. Therefore, it is investigated whether user-oriented technologies, such as augmented reality (AR) and virtual reality (VR) can be applied “on-the-job” for such training and support—also known as intelligence augmentation (IA). To address this problem, this work synthesizes results of a systematic literature review as well as a practically oriented search on augmented reality and virtual reality use cases within the IA context. A total of 150 papers and use cases are analyzed to identify suitable areas of application in which it is possible to enhance employees' capabilities. The results of both, theoretical and practical work, show that VR is primarily used to train employees without prior knowledge, whereas AR is used to expand the scope of competence of individuals in their field of expertise while on the job. Based on these results, a framework is derived which provides practitioners with guidelines as to how AR or VR can support workers at their job so that they can keep up with anticipated skill demands. Furthermore, it shows for which application areas AR or VR can provide workers with sufficient training to learn new job tasks. By that, this research provides practical recommendations in order to accompany the imminent distortions caused by AI and similar technologies and to alleviate associated negative effects on the German labor market.
Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant to adopt these sophisticated data analytics methods as research instruments. Even the Information Systems (IS) discipline with its close connections to Computer Science seems to be conservative when conducting empirical research endeavors. To assess the magnitude of the problem and to understand its causes, we conducted a bibliographic review on publications in high-level IS journals. We reviewed 1,838 articles that matched corresponding keyword-queries in journals from the AIS senior scholar basket, Electronic Markets and Decision Support Systems (Ranked B). In addition, we conducted a survey among IS researchers (N = 110). Based on the findings from our sample we evaluate different potential causes that could explain why ML methods are rather underrepresented in top-tier journals and discuss how the IS discipline could successfully incorporate ML methods in research undertakings.
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.