Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of 'datification'

By S. Newell, Marco Marabelli. Journal of strategic information systems, 2015

474 citations recorded by the source corpus.

Today, digital data are captured through a variety of devices that have the ability to monitor the minutiae of an individual's everyday life. These data are often processed by algorithms, which support (or drive) decisions (termed 'algorithmic decision-making' in this article). While the strategic value of these data (and subsequent analysis) for businesses is unquestionable, the implications for individuals and wider society are less clear. Therefore, in this Viewpoint article we aim to shed light on the tension between businesses - that increasingly profile customers and personalize products and services - and individuals, who, as McAfee and Brynjolfsson (2012, p. 5) suggest, are 'walking data generators' but are often unaware of how the data they produce are being used, and by whom and with what consequences. Issues associated with privacy, control and dependence arise, suggesting that social and ethical concerns related to the way business is strategically exploiting digitized technologies that increasingly support our everyday activities should be brought to the fore and thoughtfully discussed. In this article we aim to lay a foundation for this discussion in the IS community and beyond.

Subfields: Management Information Systems, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/719dbcb7c24c471c8093b66d976d1973d731af06

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Citations in the corpus

The ethics of algorithms: Mapping the debate (2016, unknown): “Whether dynamic or static, the individual is comprehended based on connections with others identiﬁed by the algorithm, rather than actual behaviour (Newell and Marabelli, 2015: 5).”
Debating big data: A literature review on realizing value from big data (2017, methodology): “Indeed, some scholars contend that too much reliance on algorithms by decision makers may lead to a loss or replacement of such relevant knowledge, particularly when it is not clear how algorithms arrive at certain results, patterns, and decisions (Markus, 2015; Newell and Marabelli, 2015).”
How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service (2018, background): “These insights have the potential to create competitive advantage [6, 14, 32], and BDA is expected to support customer-oriented service innovation in a number of ways [34, 58].”
Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda (2015, methodology): “Similarly, data collected via cellphones, smart phones, ‘apps’, or sensors provide new opportunities for data gathering and radically innovative exploitation (e.g., Baesens et al., 2014; Michael and Michael, 2011; Newell and Marabelli, 2015).”
Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review (2021, background): “In the main, although related, these literatures have been developed in parallel and their analytical connections remain unconnected so that scholarly work remains partial and fragmented (Loebbecke & Picot, 2015; Newell & Marabelli, 2015).”
Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World (2020, background): “More generally, Newell and Marabelli (2015) observed that skill preserva tion strategies recommended in the case of pilots (such as extensive simulation training) are less likely to be applied to other skills (such as driving), which means that both at work and more generally as a society we may bec”
Algorithmic management in a work context (2021, background): “Newell and Marabelli (2015: 5) found that: “discriminations are increasingly being made by an algorithm, with few individuals actually understanding what is included in the algorithm or even why.””