The development and use of Artificial Intelligence (AI) has received significant attention in academia and practice alike as AI has reached, or even surpassed, the ability of humans in a variety of domains (Arrieta et al., 2020; Sugumaran et al., 2017). AI has been described as the non-human intelligence that is flexible and autonomous enough to understand and learn from data in order to achieve specific outcomes (Kaplan and Haenlein, 2019). While different definitions for ‘Artificial Intelligence’ exist, there is a general understanding that it includes learning, reasoning, and adaptation capabilities as its key features.

AI is increasingly being applied in information systems with potentially severe consequences for humans, including medical diagnostics (McKinney et al., 2020), job recruitment (Dastin, 2018), transport management (He et al., 2014), credit scoring (Wang et al. 2019), and autonomous vehicles (Grigorescu et al., 2019). Yet, despite information systems’ rich tradition as a discipline in theorizing and studying both the technical elements as well as the human aspects in designing and managing complex systems (Beydoun et al., 2019; Dwivedi et al., 2015), the social perspective within interactions between humans and AI systems is yet underresearched (Harper, 2019). Human–AI interaction necessitates researchers and practitioners to go beyond smart algorithms, as it rather requires effective coordination of complex problem solving, teamwork, communication, and joint action between the human agent and AI systems (Seeber et al., 2020). Collaboration between the human and the AI can create heterogeneous work groups (Arrieta et al., 2020; Kaplan and Haenlein, 2019), which would require shared understanding for effective collaborative outcomes (Bittner and Leimeister, 2014).

The theme of this special issue highlights the need for conceptualization and empirical study of challenges associated with the implications of AI systems. In particular, we aim to explore, theorize, and test guidelines for upholding and implementing good AI practices in designing and managing human-AI interactions. We welcome the examination at the individual, group/team, organizational or societal level of analysis across all possible domains. We solicit case studies, surveys, experiments, qualitative research, design science research, and collaborative action research among academics, executives and policy makers that illustrate innovative approaches, resolutions, and solutions to these described tensions, risks, and opportunities. We especially seek papers that offer theoretical models along with observations or evidence of consequences related to these models.

·      Theorizing Human-AI interactions at individual and organizational levels

·      Management and governance of Human-AI interactions

·      Opportunities and challenges of Human-AI interactions from the perspective of developers, users, and policy makers

·      (Cross-stakeholder) Design of Human-AI interactions

·      Ethics, legal and social issues of Human-AI interactions

·      Regulation and certification of Human-AI interactions

·      Responsibility in Human-AI Interactions

·      Privacy and data fusion in the context of AI

·      Interaction with AI through human-in-the-loop development

·      Trust in the context of Human-AI interactions

·      Transparency and explainability of AI (XAI)

·      Business value and consequences of Human-AI interactions

·      Supporting AI ethical principles during system requirements and design

Christian Meske

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Prof. Dr. Christian Meske
Assistant Professor of Information Systems - 

Digital Transformation and Strategic Information Management

Freie Universität Berlin

School of Business & Economics 

as well as

Einstein Center Digital Future (Board Member)