2019
DOI: 10.25300/misq/2019/14577
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High Reliability in Digital Organizing: Mindlessness, the Frame Problem, and Digital Operations

Abstract: Organizations facing high risks and operating in purely digital domains, such as computer security and many financial services, must meet two, contradictory goals: they need to identify digital threats at scale and speed while also avoiding errors that result from automated processing. Research on high-reliability organizations has identified multiple challenges in reaching these goals simultaneously, because automation often renders organizations' operations "mindless" and unable to cope gracefully with chang… Show more

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Cited by 69 publications
(98 citation statements)
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“…Salovaara et al [18] studied digital high-reliability organisation, which had automated malware detection into an algorithm. When the algorithm could not handle a novel threat, human experts took over since they were capable of mindful actions.…”
Section: Individual Mindfulness and Mindlessnessmentioning
confidence: 99%
See 1 more Smart Citation
“…Salovaara et al [18] studied digital high-reliability organisation, which had automated malware detection into an algorithm. When the algorithm could not handle a novel threat, human experts took over since they were capable of mindful actions.…”
Section: Individual Mindfulness and Mindlessnessmentioning
confidence: 99%
“…When the algorithm could not handle a novel threat, human experts took over since they were capable of mindful actions. Routines in the shape of standard operating procedures or algorithms can restrain noticing novel problems, but on the other hand they may allow mindfulness in complex problem areas [18]. Although the task had multiple restrictions, the students had to know how to use a search engine.…”
Section: Individual Mindfulness and Mindlessnessmentioning
confidence: 99%
“…First, from an operational perspective, human designers delegate a design task to an autonomous tool, set parameters, start the autonomous design tool, and evaluate the outcome and make adjustments to the set of input parameters. Second, considering that problem spaces are evolving and that the same tool might be used for different design situations (and hence problem spaces), autonomous design tools suffer from the frame problem, which describes how algorithms are constrained by the rules (i.e., the knowledge) they currently possess and are hence incapable of reacting to environment states for which they are not prepared (Dennett, 2006;McCarthy & Hayes, 1981;Salovaara, Lyytinen, & Penttinen, 2019). In the case of symbolic approaches, the frame problem would demand that rules are added to the embedded design model to make it applicable to a broader or changing set of design problems (Dennett, 2006).…”
Section: The Context Of Autonomous Design Tools: a Control Perspectivementioning
confidence: 99%
“…However, even if we assume that we can infinitely add rules, such approach will increase the system's complexity and render its performance useless. While connectionist approaches involve learning, they still suffer from the frame problem as they are typically solving "closed-world" problems and remain constrained by the specific goal functions and available data (Salovaara et al, 2019). Therefore, in this section, we turn to the interaction between autonomous design tools and their control units, most notably human designers, in relation to the environment in which they operate.…”
Section: The Context Of Autonomous Design Tools: a Control Perspectivementioning
confidence: 99%
“…However, most of these studies are conducted in the organizational context and are considered organizational factors and interventions [ 22 ]. Another stream of research focuses on mindfulness in a collective context, such as a group setting, rather than examining mindfulness at the individual level [ 23 ]. Thus, more studies are required to examine the antecedents and resultant effects of IT mindfulness at the individual level in the context of using consumer IT.…”
Section: Introductionmentioning
confidence: 99%