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As Artificial Intelligence (AI) is becoming more prevalent in everyday work settings, the Information Systems (IS) discipline is perfectly poised to study the sociotechnical repercussions of AI supported decision-making part of expert's knowledge work. As expert know-how is tacit and socially situated, there are difficulties in capturing the nuances of the emerging human-machine collaborations. In this article, we review how epistemic uncertainty is evident in experts' decision-making practices when using AI tools. Building on rich primary studies, a qualitative evidence synthesis (QES) approach was used to synthesize relevant literature on this topic. Our findings unravel sources of expert uncertainty which point to strategies experts use to cope with uncertainty, and what attitudes experts have towards uncertainty when making complex decisions with AI-tools. [ABSTRACT FROM AUTHOR]

Establishing accountability for Artificial Intelligence (AI) systems is challenging due to the distribution of responsibilities among multiple actors involved in their development, deployment, and use. Nonetheless, AI accountability is crucial. As AI can affect all aspects of private and professional life, the actors involved in AI lifecycles need to take responsibility for their decisions and actions, be ready to respond to interrogations by those affected by AI and held liable when AI works in unacceptable ways. Despite the significance of AI accountability, the Information Systems research community has not engaged much with the topic and lacks a systematic understanding of existing approaches to it. This paper present the results of a comprehensive conceptual literature review that synthetizes current knowledge on AI accountability. The paper contributes to the IS literature by providing (i) conceptual clarification mapping different accountability conceptualizations; (ii) a comprehensive framework for AI accountability challenges and actionable responses at three different levels: system, process, data and; (iii) a framing of AI accountability as a a socio-technical and organizational problem that IS researchers are well-equipped to study highlighting the need to balance instrumental and humanistic outcomes. [ABSTRACT FROM AUTHOR]

In 1995, marine biologist Daniel Pauly warned against shifting baseline syndrome, or the tendency to assess environmental sustainability against biased or inappropriate baseline data. Today, digital systems have a strong potential to make new Green Information Systems (IS) solutions possible. However, these systems also require in-depth analysis, to prevent Green IS from falling into shifting baseline syndrome. We draw on a qualitative study of distributed systems for environmental monitoring during oil and gas operations. We problematize the creation of baseline data against which the environmental sustainability of operations is calculated. Based on a performative understanding of technology in practice, we illustrate the materialdiscursive conditions of the data production and use. We present four recommendations for future research in Green IS, inviting researchers to critically investigate the way in which sustainable business processes are mutually constituted by the digital technologies, monitoring practices, and knowledge in organizations and societies. [ABSTRACT FROM AUTHOR]

Networks of smart devices referred to as Internet of Things (IoT) have valuable applications across policy areas in the public sector. However, we lack knowledge on how IoT actually takes part in processes of societal decision making and its social implications. In this paper, we report from research in progress on IoT and big data in the public sector. Empirically, we study three cases of utilization of IoT in three domains where sensors and wireless infrastructures are put in place. Specifically, the case studies are in the contexts of city management (smart city), healthcare and environmental monitoring. Theoretically we make use of the concept of infrastructuring to go beyond the focus on IoT infrastructures as networks of technical objects, and rather foreground IoT as political, value-laden and performative configurations. We focus on algorithmic phenomena to examine the processes by which algorithms shape reality. Our preliminary findings show that this is a promising direction for research. This study will contribute to improve the understanding of how IoT arrangements transform processes of governance in the public sector. [ABSTRACT FROM AUTHOR]

This AAT symposium proposal is a response to the AOM 2021 CFP (theme: ""Bringing the Manager back in Management""): ""What technologies and methodologies will help us better understand managerial issues?.... Supply chains and other operations will be affected. How will firms' competitive and comparative advantages, core competencies, and dynamic capabilities change? Where will new competitors come from and how will corporate strategies change in the future?"" (Hillman 2020, p. 1). In MC, OCIS and MED AOM divisions, different approaches have been developed to investigate the research question of digital transformation value (DTV). The three divisions recently collaborated during and an AAT symposium on the topic of ""artificial intelligence value"" during the annual meeting of the AOM 2020 in Vancouver PDW and on a PDW on ""artificial intelligence in management"" during the AOM 2018 annual meeting in Boston. These divisions also organized pre-AOM conference on digital transformation at Harvard University in 2019 and 2020 in a virtual mode prior to AOM 2020 Vancouver. During these events, the three divisions compared their different research tradition on artificial intelligence value. These divergence are today extended to the broader field of digital transformation. In a nutshell, the keywords used by each divisions indicate how different may be the respective viewpoints. Whereas OCIS draw from the the business value of IT and IT success and failures or changing nature of work, MC rather consider the impact of technology, socio-economic approaches to management and organizational development. From the standpoint of MED, these issues raise research questions the future of management education including the role of business schools and MBA/MSc classes and programs. More precisely, how is digital transformation value approached in each division? In OCIS division, this digital transformation value is investigated through the concepts of process-oriented approach for evaluating the business value (Mooney, Gurbaxani, and Kraemer 1996; Lynn, Mooney, Rosati & Cummins 2019), or cost savings due to IT outsourcing either or not related to cloud platforms (Bapna et al., 2016), hidden work related to artificial intelligence in digital transformation projects (Watson-Manheim & Klein 2019), gain-share contracts in coordination in customer support center (Bhattacharya et al., 2014) or the strength of ties in online social networks (Bapna et al., 2017). In MC division, while the impact of technology and resistance to change have been subject to a growing number of communications during recent AOM annual meetings (Monod et al., 2018; Monod et al., 2019a; 2019b), one of the most promising approach to investigate digital transformation value seem to be related to hidden costs and Socio-Economic Approach to Management (Savall & Zardet, 2013; Savall et al., 2019) and their connection to organization development (Sanders, 2019). Beyond cost and benefit calculations, the future of management education may be at stake with AI. In the context of the crisis of business school described in AMLE (Pfeffer & Fong 2002; Pfeffer & Sutton 2006; Henisz, 2011), the way business schools will be able to catching competitive advantage (Eisner, 2003) relies in a great extend on the way they may take the lead on AI, renew their role, and include digital transformation in their MBA/MSc and DBA classes and programs (Eisner & Monod, 2019). This move would contribute to business school professors' credibility with practitioners (Ritter et al., 2016). [ABSTRACT FROM AUTHOR]