A Multi-Perspective Framework for Research on (Sustainable) Autonomous Systems

Beck, Roman; Dibbern, Jens; Wiener, Martin (2022). A Multi-Perspective Framework for Research on (Sustainable) Autonomous Systems. Business & information systems engineering, 64(3), pp. 265-273. Springer 10.1007/s12599-022-00752-0

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The ongoing digital transformation is challenging the way in which business is conducted and value is created and captured (Vial 2019). While prior digitalization waves focused on replacing paper as physical carrier of information, leveraging the Internet as global communication infrastructure, and developing reactive, partly automated business processes and systems (e.g. Legner et al. 2017), the next wave will be about transforming these processes/systems into proactive autonomous systems (AS). Such systems represent complex “systems of systems” with different maturities, qualities, reliabilities, and performances, which may develop their own dynamics (Boardman and Sauser 2006; Maier 1999). In the information systems (IS) context, a common characteristic of AS is their reliance on large amounts of data, along with the use of advanced technologies—such as the Internet of Things, Artificial Intelligence (AI), Machine Learning, or Blockchain—that allow for gathering and processing ‘big’ data with limited, or even no, human involvement.

Today, AS can be found in various fields of application. Popular examples include driverless cars, smart cities, and smart homes, which often rely on a combination of sensors, algorithms, and self-executable code. Besides these tangible AS that link the physical world to the information world (Barrett 2006), we note a growing number of intangible AS in the form of software systems that operate either entirely in the background or at the interface with humans. Examples are intelligent chatbots, smart contracts, and recommender systems (Murray et al. 2021a; Pfeiffer et al. 2020; Rutschi and Dibbern 2020; Wang et al. 2019a, b), as well as algorithmic management and control systems, such as the ones used by Uber and other gig economy firms to manage their digital workforce (Cram and Wiener 2020; Möhlmann et al. 2021; Wiener et al. 2021).

Even though AS are designed, developed, and implemented in a process of socio-technical interaction, once in use, the embedded technology takes on the role of an autonomous agent (or actor) that can make decisions and perform actions independently of humans (Baird and Maruping 2021). In other words, what has been created in a socio-technical way by implementing patterns—including organizational rules, as well as social norms and values—into a technical system, turns into a techno-social system once operating, where social agents in the organizational environment respond to the technical system and where the system may self-adapt to environmental changes. Thus, agency, decision rights, and responsibility are handed over to technology agents, while the ultimate accountability and decision rights to change these systems still reside with the governing entity owning those systems (Kellogg et al. 2020).Footnote1 This asks for a better understanding of AS in a broader context, where the autonomy of technical systems as agents must be analyzed in relation to human agents. In fact, changes in the autonomy of one (human or technology) agent may have consequences for the autonomy of another agent. Accordingly, the notion of “conjoined agency” between human and technology agents has been conceptualized as one way to acknowledge new types of interdependencies that arise in the course of increasing technology autonomy (Murray et al. 2021b).

Another way to view AS is by consideration of their temporal dimension, as captured by the notion of sustainability, which generally refers to some long-term existence. This means that, once in use, AS should be able to exist and technology agents embedded in these systems should be able to fulfill their function for a longer period of time without human intervention, as otherwise they cannot be considered being really autonomous. In this sense, sustainable autonomous systems (SAS) may refer to self-learning technical systems that are constantly improving themselves, such as an autonomous vehicle that, on a daily commute, keeps optimizing the route it takes. Put differently, SAS are characterized by their ability to adapt to changing circumstances and be responsive to environmental changes. In doing so, SAS may not only optimize themselves in accordance with some predefined output criteria (e.g., quality or performance), but also with regard to their consumption of resources (e.g., an autonomous vehicle constantly improving its fuel consumption). On a larger scale, this points to another perspective on sustainability directed towards the effects of AS use and operation. As such, sustainability may also concern the long-term economic, social, and environmental effects of using AS (Hart and Milstein 2003), commonly referred to as the “3Ps” (profit, people, and planet) of the triple bottom line (Elkington 1997). This perspective includes the effects of SAS on the efficient use of tangible resources, such as energy (e.g., smart offices), space (e.g., smart cities), food (e.g., smart fridges), or natural resources (e.g., smart agricultures), as well as their effects on intangible resources, such as the longevity of data (e.g., for auditing purposes) or human and social capital in general.

While the debate around SAS is not new, the emergence of blockchain has fueled innovative solutions, but also concerns regarding the energy consumption of blockchains based on the so-called “proof of work” consensus mechanism (Sedlmeir et al. 2020). While ecologic sustainability is one important aspect of SAS, there are further aspects that need to be considered. For example, as unintended and unforeseen second-order or spillover effects can result from the deployment of SAS, the question must be answered if we really want to rely on systems that are on ‘autopilot.’ Here, critical ethical questions arise (Tang et al. 2020), including questions of fairness regarding the decision rules according to which AS act (Dolata et al. 2021); for instance, how a driverless car should react to unforeseen circumstances affecting humans (Kirkpatrick 2015).

In recent years, IS research has begun to pick up the concept of autonomy and to study it from different perspectives. Thereby it is important to note that the concept of autonomy is by no means new to the IS field. For example, autonomy has been an inherent characteristic of intelligent software agents (Jennings et al. 1998), which have been subject of research in various fields of application, such as supply-chain automation and improvement (Nissen and Sengupta 2006) or electronic auctions (Adomavicius et al. 2008). It is only recently, however, that the concept of autonomy has gained increasing interest with regard to the phenomena described above.

Against this backdrop, in this editorial, we seek to synthesize and integrate different autonomy concepts and develop a framework that can serve as a basis for future research on (S)AS in various IS contexts and settings. In particular, drawing on the IS and related literatures, we first identify and review different autonomy concepts and their definitions. On this basis, we then elaborate on the relationships among those concepts and present a multi-perspective framework for studying (S)AS in a broader “systems of systems” context along with promising directions for future research. Our framework has been inspired by the existing literature on autonomy and AS, as well as the experiences we made as editors during the review process for our special issue on SAS in BISE. In total, we received 12 papers out of which two were accepted and published in this issue.

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