Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "15198"
#_source: array:26 [
"id" => "15198"
"slug" => "15198-relational-expertise-what-machines-cant-know"
"yearMonth" => "2023-01"
"year" => "2023"
"title" => "Relational Expertise: What Machines Can't Know"
"description" => "PAKARINEN, P. et HUISING, R. (2023). Relational Expertise: What Machines Can't Know. <i>Journal of Management Studies</i>, In press. 
PAKARINEN, P. et HUISING, R. (2023). Relational Expertise: What Machines Can't Know. <i>Journal of M "
"authors" => array:2 [
0 => array:3 [
"name" => "HUISING Ruthanne"
"bid" => "B00819530"
"slug" => "huising-ruthanne"
]
1 => array:1 [
"name" => "Pakarinen Pauli"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2024-09-30 13:56:35"
"publicationUrl" => "https://doi.org/10.1111/joms.12915"
"publicationInfo" => array:3 [
"pages" => ""
"volume" => "In press"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Professions continue to be the primary means through which societies institutionalize expertise. Recent analyses and narratives predict that artificial intelligence (AI) will make meaningful inroads into non-routine reasoning about complex cases, threatening the authority of professions. These predictions, we argue, draw on substantialist understandings of expertise as an intellectual possession, a mental achievement, or a cognitive state performed – by humans or machines – to achieve effects. A synthesis of empirical studies shows that expertise is more accurately conceptualized as relationally constituted – generated, applied, and recognized – through interactions. Relational expertise creates challenges of opacity, translation, and accountability for the development and deployment of AI technologies in the context of professional work. A relational understanding of expertise disrupts notions that professions may be augmented with, subordinated to, or dismantled by AI technologies. Instead, AI technologies are embedded in the network of interactions through which the relational expertise of professions is constituted. Professions continue to be the primary means through which societies institutionalize expertise. Rec "
"en" => "Professions continue to be the primary means through which societies institutionalize expertise. Recent analyses and narratives predict that artificial intelligence (AI) will make meaningful inroads into non-routine reasoning about complex cases, threatening the authority of professions. These predictions, we argue, draw on substantialist understandings of expertise as an intellectual possession, a mental achievement, or a cognitive state performed – by humans or machines – to achieve effects. A synthesis of empirical studies shows that expertise is more accurately conceptualized as relationally constituted – generated, applied, and recognized – through interactions. Relational expertise creates challenges of opacity, translation, and accountability for the development and deployment of AI technologies in the context of professional work. A relational understanding of expertise disrupts notions that professions may be augmented with, subordinated to, or dismantled by AI technologies. Instead, AI technologies are embedded in the network of interactions through which the relational expertise of professions is constituted. Professions continue to be the primary means through which societies institutionalize expertise. Rec "
]
"authors_fields" => array:2 [
"fr" => "Management"
"en" => "Management"
]
"indexedAt" => "2025-04-02T15:21:40.000Z"
"docTitle" => "Relational Expertise: What Machines Can't Know"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/huising-ruthanne">HUISING Ruthanne</a>, Pakarinen Pauli"
"docDescription" => "<span class="document-property-authors">HUISING Ruthanne, Pakarinen Pauli</span><br><span class="document-property-authors_fields">Management</span> | <span class="document-property-year">2023</span> <span class="document-property-authors">HUISING Ruthanne, Pakarinen Pauli</span><br><span class="doc "
"keywordList" => ""
"docPreview" => "<b>Relational Expertise: What Machines Can't Know</b><br><span>2023-01 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1111/joms.12915" target="_blank">Relational Expertise: What Machines Can't Know</a> <a href="https://doi.org/10.1111/joms.12915" target="_blank">Relational Expertise: What Machines Can "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.984276
+"parent": null
}
