A Modeling Approach for Measuring the Performance of a Human-AI Collaborative Process
dc.contributor.author | Sankaran, G | |
dc.contributor.author | PALOMINO, MARCO | |
dc.contributor.author | Knahl, M | |
dc.contributor.author | Siestrup, G | |
dc.date.accessioned | 2022-11-17T17:38:28Z | |
dc.date.available | 2022-11-17T17:38:28Z | |
dc.date.issued | 2022-11-16 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.other | ARTN 11642 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/19986 | |
dc.description.abstract |
Despite the unabated growth of algorithmic decision-making in organizations, there is a growing consensus that numerous situations will continue to require humans in the loop. However, the blending of a formal machine and bounded human rationality also amplifies the risk of what is known as local rationality. Therefore, it is crucial, especially in a data-abundant environment that characterizes algorithmic decision-making, to devise means to assess performance holistically. In this paper, we propose a simulation-based model to address the current lack of research on quantifying algorithmic interventions in a broader organizational context. Our approach allows the combining of causal modeling and data science algorithms to represent decision settings involving a mix of machine and human rationality to measure performance. As a testbed, we consider the case of a fictitious company trying to improve its forecasting process with the help of a machine learning approach. The example demonstrates that a myopic assessment obscures problems that only a broader framing reveals. It highlights the value of a systems view since the effects of the interplay between human and algorithmic decisions can be largely unintuitive. Such a simulation-based approach can be an effective tool in efforts to delineate roles for humans and algorithms in hybrid contexts. | |
dc.format.extent | 11642-11642 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | MDPI AG | |
dc.subject | machine learning | |
dc.subject | system dynamics | |
dc.subject | simulation modeling | |
dc.subject | algorithmic decision-making | |
dc.subject | bounded rationality | |
dc.subject | supply chain planning | |
dc.title | A Modeling Approach for Measuring the Performance of a Human-AI Collaborative Process | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000887048500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 22 | |
plymouth.volume | 12 | |
plymouth.publication-status | Published online | |
plymouth.journal | Applied Sciences | |
dc.identifier.doi | 10.3390/app122211642 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dcterms.dateAccepted | 2022-11-12 | |
dc.rights.embargodate | 2022-11-19 | |
dc.identifier.eissn | 2076-3417 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.3390/app122211642 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review |