‘Thinking Problematically’ as a Resource for AI Design in Politicised Contexts

Petersen, Anette C. M.; Cohn, Marisa Leavitt; Hildebrandt, Thomas; Møller, Naja L. Holten

doi:10.1145/3464385.3464738

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…In the end, Batman always remains the primary decision-maker in the collaboration while the less-skilled Robin remains the sidekick. Within the charismatic hype and hope broadly associated with AI that envisions AI as an equal or superior entity to humans (Petersen et al, 2021), human-AI would seem to strive for producing a second (maybe even a better) AI-Batman to act in concert with human-Batman. Such charismatic visions take on a life of their own, influencing design and evaluation of technologies (Petersen et al, 2021).…”

Section: Human-ai Collaboration As Batman and Robin (Not Batman And B...mentioning

confidence: 99%

“…Within the charismatic hype and hope broadly associated with AI that envisions AI as an equal or superior entity to humans (Petersen et al, 2021), human-AI would seem to strive for producing a second (maybe even a better) AI-Batman to act in concert with human-Batman. Such charismatic visions take on a life of their own, influencing design and evaluation of technologies (Petersen et al, 2021). Yet, the case of the CDIS demonstrates that developing a vision of a human Batman supported by an AI Robin unremarkable both in terms of its unobtrusiveness in work practices and its capabilities may be a more accurate picture of successful human-AI collaboration.…”

Section: Human-ai Collaboration As Batman and Robin (Not Batman And B...mentioning

confidence: 99%

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Batman and Robin in Healthcare Knowledge Work: Human-AI Collaboration by Clinical Documentation Integrity Specialists

Bossen

Pine

2023

ACM Trans. Comput.-Hum. Interact.

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This paper describes the successful collaboration ‘in the wild’ between Clinical Documentation Integrity Specialists (CDIS) and an Artificial Intelligence (AI)-embedded software to conduct knowledge work. CDIS review patient charts in near real time to improve clinicians’ documentation, with the goal to make medical documentation more accurate, consistent and complete. CDIS collaborate with an AI-embedded “Computer Assisted Coding” (CAC) system that scans records from the Electronic Healthcare Record and auto-suggests codes based on natural language processing. CDIS find the CAC's suggestions are often inaccurate—often humorously so. Still, they find the CAC to be a useful helper, like Robin is to Batman. This human-AI collaboration is contingent on several factors: the flexible integration of the AI into the workflow similar to the notion of unremarkable AI; supporting the CDIS’ sensemaking; the CDIS’ knowledge about the CAC being predictably unreliable, an experience by the CDIS of the AI's value; humans remaining in control; and ability to experiment with the AI, which spurs reflection and learning for these knowledge workers.

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Section: Human-ai Collaboration As Batman and Robin (Not Batman And B...mentioning

confidence: 99%

Section: Human-ai Collaboration As Batman and Robin (Not Batman And B...mentioning

confidence: 99%

Batman and Robin in Healthcare Knowledge Work: Human-AI Collaboration by Clinical Documentation Integrity Specialists

Bossen

Pine

2023

ACM Trans. Comput.-Hum. Interact.

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“…Last, but not least, we take from CDS and other schools of critical theory the need to actively 'problematize' intra-disciplinary perspectives and constructions in order to reflexively engage disciplinary biases in the design of computational legal methods (Petersen et al 2021). A point similarly recognised in wider debates on interdisciplinary engagements between law and the social sciences (Klabbers 2009;Noll 2005: 6).…”

Section: Towards Reflexive Strategies In Computational Refugee Law Re...mentioning

confidence: 99%

Data-Driven Futures of International Refugee Law

Byrne

Gammeltoft‐Hansen

Piccolo

et al. 2023

Journal of Refugee Studies

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As refugee law practice enters the world of data, it is time to take stock as to what refugee law research can gain from technological developments. This article provides an outline for a computationally driven research agenda to tackle refugee status determination variations as a recalcitrant puzzle of refugee law. It first outlines how the growing field of computational law may be canvassed to conduct legal research in refugee studies at a greater empirical scale than traditional legal methods. It then turns to exemplify the empirical purchase of a data-driven approach to refugee law through an analysis of the Danish Refugee Appeal Board’s asylum case law and outlines methods for comparison with datasets from Australia, Canada, and the United States. The article concludes by addressing the data politics arising from a turn to digital methods, and how these can be confronted through insights from critical data studies and reflexive research practices.

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“…Mistilliden til faglig dømmekraft: en klassisk problemstilling i professions-og ledelsesstudier Dataarbejde og samarbejde med kunstigt intelligente løsninger blandt velfaerdsprofessionelle er kompliceret (Petersen et al, 2021;Flügge, Hildebrandt og Møller, 2021;Møller, Shklovski og Hildebrandt, 2020;Kirkegaard, Kristensen og Lauridsen, 2022). I årtier har de velfaerdsprofessionelles arbejde med at vurdere og beslutte borgerens velfaerdsforløb eller adgang til ydelser vaeret problematiseret og debatteret (fx Lipsky, 2010;Zacka, 2017;Tummers et al, 2015;Maynard-Moody og Musheno, 2000, 2003Jorna og Wagenaar, 2007).…”

unclassified

Drømmen om datadrevet velfærd

Pors,

Pedersen,

Kirkegaard

2023

politica

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Hvordan formes drømmen om potentialet i data og kunstig intelligens blandt ledere i den offentlige forvaltning, og hvilke roller tildeles frontlinjemedarbejderen i den datadrevne velfærd? Baseret på observationer af kommunale ledere og konsulentoplæg undersøger artiklen de datadrømme, som omgærder udviklingsarbejdet med at introducere kunstig intelligens i kommunale beskæftigelsesindsatser. Deres datadrømme indebærer en kritik af frontlinjemedarbejderen, som mimer den etablerede ledelses- og professionsforskning, hvor frontlinjemedarbejderens praksis problematiseres: I datadrømmene kritiseres frontlinjemedarbejderens dømmekraft for i udstrakt grad alene at være baseret på menneskelig erfaring, individuelle mestringsstrategier eller professionsbårne interesser og derfor ikke varetage borgerens behov, hvilket gør, at den organisatoriske værdiskabelse forringes.

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‘Thinking Problematically’ as a Resource for AI Design in Politicised Contexts

Cited by 10 publications

References 39 publications

Batman and Robin in Healthcare Knowledge Work: Human-AI Collaboration by Clinical Documentation Integrity Specialists

Batman and Robin in Healthcare Knowledge Work: Human-AI Collaboration by Clinical Documentation Integrity Specialists

Data-Driven Futures of International Refugee Law

Drømmen om datadrevet velfærd

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