Journal article 78 views
Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts
Ben Wilson 
,
Chiara Natali,
Matt Roach ,
Darren Scott,
Alma Rahat ,
David Rawlinson,
Federico Cabitza
,
Chiara Natali,
Matt Roach ,
Darren Scott,
Alma Rahat ,
David Rawlinson,
Federico Cabitza
Computer Supported Cooperative Work (CSCW)
Swansea University Authors:
Ben Wilson , Matt Roach , Darren Scott, Alma Rahat
Abstract
Whilst it is commonly reported that healthcare is set to benefitfrom advances in Artificial Intelligence (AI), there is a consensus that, forclinical AI, a gulf exists between conception and implementation. Here weadvocate the increased use of situated design and evaluation to close thisgap, showing...
| Published in: | Computer Supported Cooperative Work (CSCW) |
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| Published: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa69051 |
| Abstract: |
Whilst it is commonly reported that healthcare is set to benefitfrom advances in Artificial Intelligence (AI), there is a consensus that, forclinical AI, a gulf exists between conception and implementation. Here weadvocate the increased use of situated design and evaluation to close thisgap, showing that in the literature there are comparatively few prospectivesituated studies. Focusing on the combined human-machine decision-makingprocess - modelling, exchanging and resolving - we highlight the need foradvances in exchanging and resolving. We present a novel relational space -contextual dimensions of combination - a means by which researchers,developers and clinicians can begin to frame the issues that must beaddressed in order to close the chasm. We introduce a space of eight initialdimensions, namely participating agents, control relations, task overlap,temporal patterning, informational proximity, informational overlap, inputinfluence and output representation coverage. We propose that our awarenessof where we are in this space of combination will drive the development ofinteractions and the designs of AI models themselves. Designs that takeaccount of how user-centered they will need to be for their performance to betranslated into societal and individual benefit. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
Engineering and Physical Sciences Research Council grant EP/S021892/1
EMRTS, Cymru.
Swiss Government Excellence Scholarship (ESKAS No.2024.0002) for the academic year 2024-25.
PRIN PNRR 2022 InXAID - Interaction with eXplainable Artificial
Intelligence in (medical) Decision making. CUP: H53D23008090001
Grant Agreement no. 101120763 - TANGO. |