AI and Future of Work

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    The AI Family: The Information Security Managers Best Frenemy?
    ( 2022-01-04) De Nobrega, Kristel ; Rutkowski, A.-F.
    In this exploratory study, we deliberately pull apart the Artificial from the Intelligence, the material from the human. We first assessed the existing technological controls available to Information Security Managers (ISMs) to ensure their in-depth defense strategies. Based on the AI watch taxonomy, we then discuss each of the 15 technologies and their potential impact on the transformation of jobs in the field of security (i.e., AI trainers, AI explainers and AI sustainers). Additionally, in a pilot study we collect the evaluation and the narratives of the employees (n=6) of a small financial institution in a focus group session. We particularly focus on their perception of the role of AI systems in the future of cyber security.
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    Task Delegability to AI: Evaluation of a Framework in a Knowledge Work Context
    ( 2022-01-04) Cvetkovic, Izabel ; Bittner, Eva
    With the increased research focus on ways to use AI for augmentation rather than automation of knowledge-intensive work, a myriad of questions on how this should be accomplished arises. To break down the complexity of Human-AI collaboration, this paper pursues the identification of factors that contribute to the delegation of tasks to AI in such a setting, and consequently gain insights into requirements for meaningful task allocation. To address this research gap, we carried out an empirical study on an existing task delegability framework in a knowledge work context. We employed several statistical approaches such as confirmatory factor analysis, linear regression, and analysis of covariance. Results show that an adapted framework with fewer factors fits the data better. As for the framework factors, we show that the factor trust predicts delegability best. Furthermore, we find a significant impact of task on delegability decision. Finally, we derive theoretical and design implications.
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    Requirements for AI-based Teammates: A Qualitative Inquiry in the Context of Creative Workshops
    ( 2022-01-04) Elshan, Edona ; Siemon, Dominik ; De Vreede, Triparna ; De Vreede, Gert-Jan ; Oeste-Reiß, Sarah ; Ebel, Philipp
    Innovation requires organizations to tap into the knowledge and creativity of teams. However, teams are confronted with massive amounts of data and information, necessitating a broad set of knowledge, methodologies, and approaches to solve problems and innovate. Consequently, team composition has become a critical challenge. Recent advances in artificial intelligence (AI) may assist in addressing this challenge. As AI is permeating both business and private sectors, organizational teams may be augmented with AI team members. However, given the nascent nature of this phenomenon, little is known about the specific roles and requirements for such AI teammates. Within an interview study we discover common challenges in teams and identify recurring capability gaps of participants and behaviors that negatively impact the team's collective performance. Based on our findings, we propose requirements for AI-based teammates to address these gaps and support beneficial collaboration between humans and AI in teams.
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    Impact of Robotic Process Automation on Future Employment of Accounting Professionals
    ( 2022-01-04) Lui, Gladie ; Shum, Connie
    The COVID-19 pandemic created an increased need for companies to use Robotic Process Automation (RPA) for efficiency and cost cutting. Most companies that utilize RPA usually start with their accounting and finance departments because of the routine and rule-based nature of accounting and finance functions. Consequently, accounting job displacement by the RPA implementation becomes unavoidable. The primary objective of this paper is to explore the perception of accounting students regarding the impact of RPA on the accounting profession. Results show that there is an expectation gap between students’ expectation and the real-world scenario of accounting jobs being replaced by RPA. However, after engaging in learning activities in the accounting course used in this study, students become more aware that RPA can displace accountants. Their perceptions become more in line with that of the real-world scenario.
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    Ethics Guidelines for Using AI-based Algorithms in Recruiting: Learnings from a Systematic Literature Review
    ( 2022-01-04) Hofeditz, Lennart ; Mirbabaie, Milad ; Luther, Audrey ; Mauth, Riccarda ; Rentemeister, Ina
    To reduce the workload of employees working in Human Resource departments and to avoid bias in pre-selection of applicants, an increasing number of companies deploy Artificial Intelligence (AI)-based algorithms. Some examples such as Amazon’s discriminating recruiting algorithm showed that algorithms are not free of unethical decision making. Although there already exists a variety of ethics principles for AI-based systems, those are usually hardly being applicable to specific use cases such as using AI-based algorithms in recruiting processes. To address this issue and to provide guidance for researchers and practitioners, we conducted a systematic literature review (keyword and backwards search) on existing ethics guidelines and principles for AI and extracted aspects that seemed applicable to guide recruiting processed. Based on 28 relevant papers we derived actionable guidelines for using AI-based algorithms in recruiting processes. We categorized our guidelines into the aspects of fairness, avoidance of discrimination and avoidance of bias.