Algorithmic Finance, Its Regulation, and Deleuzean Jurisprudence: A Few Remarks on a Necessary Paradigm Shift

Lenglet, Marc

doi:10.1007/s11245-019-09653-6

Cited by 7 publications

(7 citation statements)

References 44 publications

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“…Moreover, even with a goal of transparency, creating machines that divulge and explain their behavior in way humans can interpret is difficult. Data disclosure by the machine as it operates can provide some means for monitoring (Lenglet, 2019), but fast machines require fast monitoring to keep up: this in turn calls for more systems! In principle, monitoring computers can better report to humans as now seen in smart watches, phones, and clickstream analyzers.…”

Section: Monitoringmentioning

confidence: 99%

Metahuman systems = humans + machines that learn

Lyytinen

Nickerson

King

2020

Journal of Information Technology

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Metahuman systems are new, emergent, sociotechnical systems where machines that learn join human learning and create original systemic capabilities. Metahuman systems will change many facets of the way we think about organizations and work. They will push information systems research in new directions that may involve a revision of the field’s research goals, methods and theorizing. Information systems researchers can look beyond the capabilities and constraints of human learning toward hybrid human/machine learning systems that exhibit major differences in scale, scope and speed. We review how these changes influence organization design and goals. We identify four organizational level generic functions critical to organize metahuman systems properly: delegating, monitoring, cultivating, and reflecting. We show how each function raises new research questions for the field. We conclude by noting that improved understanding of metahuman systems will primarily come from learning-by-doing as information systems scholars try out new forms of hybrid learning in multiple settings to generate novel, generalizable, impactful designs. Such trials will result in improved understanding of metahuman systems. This need for large-scale experimentation will push many scholars out from their comfort zone, because it calls for the revitalization of action research programs that informed the first wave of socio-technical research at the dawn of automating work systems.

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Section: Monitoringmentioning

confidence: 99%

Metahuman systems = humans + machines that learn

Lyytinen

Nickerson

King

2020

Journal of Information Technology

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“…Sewell and Wilkinson, 1992), a form of care work rendered passive and reactive by the performative nature of algorithmic technology (cf. Lenglet, 2019), and shaped by protocol-driven approaches to establishing 'reliable' care processes. In our case this appears in part a problem of the mismatch between chronic and continuing experiences of illness, which AKI transverses.…”

Section: Discussionmentioning

confidence: 99%

“…However, the analytical leap from a recursive algorithm to a recursive logic of organisation appears to entangle organisational politics and procedures in questionable ways (Neyland, 2014). As has been shown in other domains, algorithmic technology is disrupting times and spaces in a manner which makes problematic the interpretive work of bureaucratic stabilising and making visible (Lenglet, 2019). This puts into question the status of, and relationship between, the different kinds of formal and informal work instituted by algorithms, their coordination, and the different images of organisation with which we are left.…”

Section: Algorithms Medicine and Workmentioning

confidence: 99%

“…Our broad focus is therefore upon organising the digital, and within this, what we have termed algorithmic work: the work that people do to support the functioning of the algorithm. As sociologists of organisation, the convergence point between algorithmic work and other kinds of work interests us because different kinds of work require different coordination, yet coordination has almost exclusively been premised on the assumption that technology is an extension of human capacities, not that technology can have an independent reality of its own that might even be inaccessible to humans (Lenglet, 2019). There is a growing recognition among social scientists of the challenges to governance and regulation posed by algorithms, manifested in their capacity for secrecy, opacity and inscrutability (Pasquale, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Dismembering organisation: The coordination of algorithmic work in healthcare

et al. 2020

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Algorithms are increasingly being adopted in healthcare settings, promising increased safety, productivity and efficiency. The growing sociological literature on algorithms in healthcare shares an assumption that algorithms are introduced to ‘support’ decisions within an interactive order that is predominantly human-oriented. This article presents a different argument, calling attention to the manner in which organisations can end up introducing a non-negotiable disjuncture between human-initiated care work and work that supports algorithms, which the authors call algorithmic work. Drawing on an ethnographic study, the authors describe how two hospitals in England implemented an Acute Kidney Injury (AKI) algorithm and analyse ‘interruptions’ to the algorithm’s expected performance. When the coordination of algorithmic work occludes care work, the study finds a ‘dismembered’ organisation that is algorithmically-oriented rather than human-oriented. In the discussion, the authors examine the consequences of coordinating human and non-human work in each hospital and conclude by urging sociologists of organisation to attend to the importance of the formal in algorithmic work. As the use of algorithms becomes widespread, the analysis provides insight into how organisations outside of healthcare can also end up severing tasks from human experience when algorithmic automation is introduced.

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“…Codes, Strategien, Verhalten 229 an deren Hervorbringung beteiligt (Lenglet 2019). Zusammen mit anderen sozialen Prozessen, wie der Produktion, der Anwendung und Nutzung, beeinflusst und koproduziert die Regulation Algorithmen.…”

Section: Regulierung In Den Rechtswissenschaften Und In Der Rechtssoz...unclassified

In digitaler Gesellschaft

2021

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Der Alltag ist durchdrungen von digitalen, algorithmen-basierten Empfehlungssystemen, die als Entscheidungsfilter für verschiedene Angelegenheiten dienen. Algorithmen geben Kaufempfehlungen, sie sortieren Informationen in Suchmaschinen und News-Feeds oder wählen eine passende Route im Straßenverkehr aus. Damit sind Algorithmen zu grundlegenden Alltagshilfen geworden, doch ihre berechneten Handlungsvorschläge zu hinterfragen, fällt schwer. 1 Je nachdem, für welche Zwecke eine Empfehlung mittels Algorithmen eingeholt wird, gibt es immer wieder Situationen, in denen es sinnvoll sein kann, die »Grammatik« (Seaver 2019) zu kennen, nach denen algorithmische Empfehlungen zustande kommen. Ein solcher Einblick in die Entwicklung von Entscheidungsalgorithmen ist für Nutzer*innen derzeit jedoch nicht möglich. Obwohl Algorithmen das Handeln in digitalisierten Alltags-und Arbeitswelten heute in vielfältiger Weise beeinflussen, ist die Art und Weise dieser Beeinflussung undurchdringlich und nicht aushandelbar. Mit Algorithmen, wie sie aktuell konstruiert werden, bleibt unklar, wer aus welchen Gründen den ein oder anderen Handlungsvorschlag erhält. Algorithmische Entscheidungssysteme verteilen Wissen neu zwischen denen, die sie nutzen, und denen, die sie programmieren und verkaufen. Diese Umverteilungen gilt es öffentlich zu verhandeln und auszuhandeln. Für eine demokratische Technikgestaltung müssen Algorithmen erklärbar werden, das bedeutet, dass unterschiedlichen Gruppen, wie etwa den Nutzer*innen selbst aber auch Vertreter*innen des Verbraucherschutzes, sozialwissenschaftlichen Forschungsansätzen oder juristischem Personal eine

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Algorithmic Finance, Its Regulation, and Deleuzean Jurisprudence: A Few Remarks on a Necessary Paradigm Shift

Cited by 7 publications

References 44 publications

Metahuman systems = humans + machines that learn

Metahuman systems = humans + machines that learn

Dismembering organisation: The coordination of algorithmic work in healthcare

In digitaler Gesellschaft

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