Inferring Work Task Automatability from AI Expert Evidence

Duckworth, Paul; Graham, Logan; Osborne, Michael A.

doi:10.1145/3306618.3314247

Cited by 13 publications

(14 citation statements)

References 8 publications

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“…The focus on task data is key, because it is individual tasks rather than entire occupations that are typically automated. [20][21][22][23][24] Recent research suggests that occupations are best analysed as evolving combinations of detailed tasks, skills and/or environments. 23 25-27 With more granular job data available than ever before, thousands of occupations can each be broken down into hundreds of numeric components or tasks, relating to the skills, knowledge and abilities required to perform them.…”

Section: Open Accessmentioning

confidence: 99%

“…A score of 4.0 indicates a 'fully automatable' work activity, while 1.0 indicates a work activity that 'cannot be automated' using currently available technology. 23 Finally, all data sources were combined to form the basis of the analysis, including: (1) the validated list of primary care tasks obtained from the ethnographic observations, interviews, and focus groups which were qualitatively coded and classified allowing us to use; (2) the corresponding O*NET activity skills, knowledge and ability numerical attributes and (3) the inferred automatability scores of these activities informed by the expert survey. A machine learning framework was employed to infer a functional mapping between the skills, knowledge and ability characteristics of work activities and the ground truth automatability elicited from the expert survey.…”

Section: Data Collection and Analysismentioning

confidence: 99%

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Qualitative and quantitative approach to assess the potential for automating administrative tasks in general practice

et al. 2020

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ObjectiveTo identify the extent to which administrative tasks carried out by primary care staff in general practice could be automated.DesignA mixed-method design including ethnographic case studies, focus groups, interviews and an online survey of automation experts.SettingThree urban and three rural general practice health centres in England selected for differences in list size and organisational characteristics.ParticipantsObservation and interviews with 65 primary care staff in the following job roles: administrator, manager, general practitioner, healthcare assistant, nurse practitioner, pharmacy technician, phlebotomist, practice nurse, pharmacist, prescription clerk, receptionist, scanning clerk, secretary and medical summariser; together with a survey of 156 experts in automation technologies.Methods330 hours of ethnographic observation and documentation of administrative tasks carried out by staff in each of the above job roles, followed by coding and classification; semistructured interviews with 10 general practitioners and 6 staff focus groups. The online survey of machine learning, artificial intelligence and robotics experts was analysed using an ordinal Gaussian process prediction model to estimate the automatability of the observed tasks.ResultsThe model predicted that roughly 44% of administrative tasks carried out by staff in general practice are ‘mostly’ or ‘completely’ automatable using currently available technology. Discussions with practice staff underlined the need for a cautious approach to implementation.ConclusionsThere is considerable potential to extend the use of automation in primary care, but this will require careful implementation and ongoing evaluation.

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Section: Open Accessmentioning

confidence: 99%

Section: Data Collection and Analysismentioning

confidence: 99%

Qualitative and quantitative approach to assess the potential for automating administrative tasks in general practice

et al. 2020

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“…In a further study, the team surveyed 156 academic and industry experts in machine learning, robotics and intelligent systems, and asked them what tasks they believed could currently be automated (Duckworth et al, 2019). They found that work that is clerical, repetitive, precise, and perceptual can increasingly be automated, while work that is more creative, dynamic, and human oriented tends to be less 'automatable'.…”

Section: Impact On the Workforcementioning

confidence: 99%

Umjetna inteligencija kao segment strategije ili značenje (uloga) umjetne inteligencije u strategijskom planiranju

Anić¹,

Anić²

2021

NSF

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Umjetna inteligencija (UI), engl. Artificial intelligence – (AI) zasigurno je najrazvikanija i najrasprostranjenija riječ u područjima znanstvenog istraživanja u proteklih nekoliko godina te je postala najzanimljivije područje razvoja. Pravovremeno prepoznavanje UI kao strategijskog opredjeljenja i investicijskog ulaganja jedno je od najzanimljivijih, ali najsloženijih područja ekonomske teorije i prakse. Usko povezana s procesom društveno ekonomskog razvitka investicija svojim multiplikativnim djelovanjem na gospodarstvo kao cjelinu, bitno opredjeljuje praktično sve ekonomske tokove pa i stanja. Strategijsko planiranje bi svakako trebalo podrazumijevati investicijsko ulaganje u UI te se u značajnoj mjeri koristiti kao mehanizam alokacije raspoloživih resursa posebno u malome gospodarstvu koje ima ograničene prirodne i privredne resurse kao što je hrvatsko.

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“…As computational systems have come to play an increasing role in making predictions about people, the downstream social consequences of artificial intelligence (AI) have garnered growing public attention. In the short term, evidence indicates that machine learning (ML) algorithms could contribute to oppression and discrimination due to historical legacies of injustice reflected in the training data [2,7,14], or directly to economic inequality through job displacement [15]. In the long term, some believe AI technologies to pose an existential risk to humanity by altering the scope of human agency and self-determination [24], or by the creation autonomous weapons [3].…”

Section: Introductionmentioning

confidence: 99%