Essec\Faculty\Model\Profile {#2188
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0 => Essec\Faculty\Model\CareerItem {#2189
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0 => Essec\Faculty\Model\Diplome {#2190
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0 => Essec\Faculty\Model\Distinction {#2193
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1 => Essec\Faculty\Model\Distinction {#2187
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2 => Essec\Faculty\Model\Distinction {#2191
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3 => Essec\Faculty\Model\Distinction {#2194
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4 => Essec\Faculty\Model\Distinction {#2195
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5 => Essec\Faculty\Model\Distinction {#2196
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6 => Essec\Faculty\Model\Distinction {#2197
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7 => Essec\Faculty\Model\Distinction {#2198
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8 => Essec\Faculty\Model\Distinction {#2199
#_index: null
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0 => Essec\Faculty\Model\TeachingItem {#2192
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0 => Essec\Faculty\Model\These {#2200
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1 => Essec\Faculty\Model\These {#2201
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2 => Essec\Faculty\Model\These {#2202
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0 => Essec\Faculty\Model\Contribution {#2204
#_index: "academ_contributions"
#_id: "14301"
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"id" => "14301"
"slug" => "in-the-land-of-the-blind-the-one-eyed-man-is-king-knowledge-brokerage-in-the-age-of-learning-algorithms"
"yearMonth" => "2022-02"
"year" => "2022"
"title" => "In the land of the blind, the one-eyed man is king: Knowledge brokerage in the age of learning algorithms"
"description" => "WAARDENBURG, L., HUYSMAN, M. et SERGEEVA, A. (2022). In the land of the blind, the one-eyed man is king: Knowledge brokerage in the age of learning algorithms. <i>Organization Science</i>, 33(1), pp. 59–82."
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0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
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1 => array:1 [
"name" => "HUYSMAN Marleen"
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2 => array:1 [
"name" => "SERGEEVA Anastasia"
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"updatedAt" => "2023-09-12 01:00:39"
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"pages" => "59–82"
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"abstract" => array:2 [
"fr" => "This paper presents research on how knowledge brokers attempt to translate opaque algorithmic predictions. The research is based on a 31-month ethnographic study of the implementation of a learning algorithm by the Dutch police to predict the occurrence of crime incidents and offers one of the first empirical accounts of algorithmic brokers. We studied a group of intelligence officers, who were tasked with brokering between a machine learning community and a user community by translating the outcomes of the learning algorithm to police management. We found that, as knowledge brokers, they performed different translation practices over time and enacted increasingly influential brokerage roles, namely, those of messenger, interpreter, and curator. Triggered by an impassable knowledge boundary yielded by the black-boxed machine learning, the brokers eventually acted like “kings in the land of the blind” and substituted the algorithmic predictions with their own judgments. By emphasizing the dynamic and influential nature of algorithmic brokerage work, we contribute to the literature on knowledge brokerage and translation in the age of learning algorithms."
"en" => "This paper presents research on how knowledge brokers attempt to translate opaque algorithmic predictions. The research is based on a 31-month ethnographic study of the implementation of a learning algorithm by the Dutch police to predict the occurrence of crime incidents and offers one of the first empirical accounts of algorithmic brokers. We studied a group of intelligence officers, who were tasked with brokering between a machine learning community and a user community by translating the outcomes of the learning algorithm to police management. We found that, as knowledge brokers, they performed different translation practices over time and enacted increasingly influential brokerage roles, namely, those of messenger, interpreter, and curator. Triggered by an impassable knowledge boundary yielded by the black-boxed machine learning, the brokers eventually acted like “kings in the land of the blind” and substituted the algorithmic predictions with their own judgments. By emphasizing the dynamic and influential nature of algorithmic brokerage work, we contribute to the literature on knowledge brokerage and translation in the age of learning algorithms."
]
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1 => Essec\Faculty\Model\Contribution {#2206
#_index: "academ_contributions"
#_id: "14302"
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"slug" => "from-coexistence-to-co-creation-blurring-boundaries-in-the-age-of-ai"
"yearMonth" => "2022-11"
"year" => "2022"
"title" => "From coexistence to co-creation: Blurring boundaries in the age of AI"
"description" => "WAARDENBURG, L. et HUYSMAN, M. (2022). From coexistence to co-creation: Blurring boundaries in the age of AI. <i>Information and Organization</i>, 32(4)."
"authors" => array:2 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
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1 => array:1 [
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0 => "Artificial intelligence"
1 => "DataTechnology and organizing"
2 => "Technology implementation"
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"updatedAt" => "2023-09-12 01:00:39"
"publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S1471772722000458"
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"fr" => "While the self-learning nature of AI systems that use machine learning calls for sustained co-creation between developers and users during development, implementation and use, information systems and management scholars still largely build on a long-established tradition of separating technology development from use. Instead, the self-learning nature of AI calls for letting go of this tradition to separate between development and use, which is starting to happen in practice but has not yet found appropriate theoretical and methodological tools among researchers. In this paper we show some real-life attempts to develop sustained collaboration among developers and users, based on empirical cases of five organizations. In particular, we propose how blurring boundaries makes data production, explainable AI and AI deployment fields of practice where development and use intertwine. We suggest embracing the blurred boundaries of AI implementation in our theorizing, understanding the different parts of AI as fields of practice where development and use come together in the co-creation of AI and work."
"en" => "While the self-learning nature of AI systems that use machine learning calls for sustained co-creation between developers and users during development, implementation and use, information systems and management scholars still largely build on a long-established tradition of separating technology development from use. Instead, the self-learning nature of AI calls for letting go of this tradition to separate between development and use, which is starting to happen in practice but has not yet found appropriate theoretical and methodological tools among researchers. In this paper we show some real-life attempts to develop sustained collaboration among developers and users, based on empirical cases of five organizations. In particular, we propose how blurring boundaries makes data production, explainable AI and AI deployment fields of practice where development and use intertwine. We suggest embracing the blurred boundaries of AI implementation in our theorizing, understanding the different parts of AI as fields of practice where development and use come together in the co-creation of AI and work."
]
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}
2 => Essec\Faculty\Model\Contribution {#2208
#_index: "academ_contributions"
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"id" => "14303"
"slug" => "addressing-key-challenges-of-developing-ai-systems-for-knowledge-intensive-work"
"yearMonth" => "2020-08"
"year" => "2020"
"title" => "Addressing key challenges of developing AI systems for knowledge intensive work"
"description" => "ZHANG, Z., NANDHAKUMAR, J., HUMMEL, J. et WAARDENBURG, L. (2020). Addressing key challenges of developing AI systems for knowledge intensive work. <i>MIS Quarterly Executive</i>, 19(4), pp. 221–238."
"authors" => array:4 [
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"name" => "WAARDENBURG Lauren"
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1 => array:1 [
"name" => "ZHANG Zoe"
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2 => array:1 [
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3 => array:1 [
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"updatedAt" => "2023-09-12 01:00:39"
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3 => Essec\Faculty\Model\Contribution {#2205
#_index: "academ_contributions"
#_id: "14304"
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"id" => "14304"
"slug" => "predictive-policing-ontcijferd-een-etnografie-van-het-criminaliteits-anticipatie-systeem-in-de-praktijk"
"yearMonth" => "2020-10"
"year" => "2020"
"title" => "Predictive policing ontcijferd: Een etnografie van het 'Criminaliteits Anticipatie Systeem' in de praktijk"
"description" => "WAARDENBURG, L., SERGEEVA, A. et HUYSMAN, M. (2020). Predictive policing ontcijferd: Een etnografie van het 'Criminaliteits Anticipatie Systeem' in de praktijk. <i>Cahiers Politiestudies</i>, (54), pp. 69–88."
"authors" => array:3 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
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1 => array:1 [
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2 => array:1 [
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"ouvrage" => ""
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"updatedAt" => "2023-10-10 14:28:36"
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"pages" => "69–88"
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"abstract" => array:2 [
"fr" => "Dit artikel beschrijft een etnografische studie naar het gebruik van statistische voorspellingen voor politiewerk (i.e., ‘predictive policing’). In dit onderzoek ‘ontcijferen’ wij zowel de input als de output van deze voorspellingen door in te gaan op hoe het gebruik van deze technologie in de praktijk tot stand komt. Dit begint bij de data scientist, die met het ontwerp van het algoritme de input en output beïnvloedt, en eindigt bij agenten op straat, die de voorspellingen wel of niet serieus nemen. Ons onderzoek gaat in tegen de algemene aanname dat een predictive policing algoritme als objectief en onafhankelijk instrument kan worden ingezet voor het verhogen van efficiëntie en effectiviteit. In plaats daarvan stellen wij dat het soms maanden werk vraagt en afhankelijk is van de handelingen en contextuele kennis van verschillende actoren (bijvoorbeeld, politieagenten, intelligence specialisten, politiemanagement, gemeente) die daarbij hun inzet en oordelen met de technologie verweven."
"en" => "This article describes an ethnographic study of the use of statistical predictions for policing (i.e., predictive policing). In this research we 'decipher' both the input and the output of these predictions by examining how the use of this technology is achieved in practice. This starts with the data scientist, who influences the input and output with the design of the algorithm, and ends with officers on the street, who may or may not take the predictions seriously. Our research goes against the general assumption that a predictive policing algorithm can be used as an objective and independent instrument to increase efficiency and effectiveness. Instead, we argue that it sometimes requires months of work and depends on the actions and contextual knowledge of various actors (for example, police officers, intelligence specialists, police management, municipality) who interweave their efforts and judgments with the technology."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
4 => Essec\Faculty\Model\Contribution {#2209
#_index: "academ_contributions"
#_id: "14305"
#_source: array:18 [
"id" => "14305"
"slug" => "managing-ai-wisely-from-development-to-organizational-change-in-practice"
"yearMonth" => "2022-10"
"year" => "2022"
"title" => "Managing AI wisely: From development to organizational change in practice"
"description" => "WAARDENBURG, L., HUYSMAN, M. et AGTERBERG, M. (2022). <i>Managing AI wisely: From development to organizational change in practice</i>. Edward Elgar Publishing Ltd."
"authors" => array:3 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
1 => array:1 [
"name" => "HUYSMAN Marleen"
]
2 => array:1 [
"name" => "AGTERBERG Marlous"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2023-09-11 14:58:20"
"publicationUrl" => "https://www.e-elgar.com/shop/gbp/managing-ai-wisely-9781800887664.html"
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Livres"
"en" => "Books"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Artificial Intelligence (AI) is being rapidly introduced into the workplace, creating debate around what AI means for our work and organizations. This book gives grounded counterweight to provocative newspaper headlines by using in-depth case studies of eight organizations’ experiences of implementing and using AI, providing readers with a solid understanding of what is actually happening in practice."
"en" => "Artificial Intelligence (AI) is being rapidly introduced into the workplace, creating debate around what AI means for our work and organizations. This book gives grounded counterweight to provocative newspaper headlines by using in-depth case studies of eight organizations’ experiences of implementing and using AI, providing readers with a solid understanding of what is actually happening in practice."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
5 => Essec\Faculty\Model\Contribution {#2203
#_index: "academ_contributions"
#_id: "14306"
#_source: array:18 [
"id" => "14306"
"slug" => "behind-the-scenes-of-artificial-intelligence-studying-how-organizations-cope-with-machine-learning-in-practice"
"yearMonth" => "2021-11"
"year" => "2021"
"title" => "Behind the scenes of artificial intelligence: Studying how organizations cope with machine learning in practice"
"description" => "WAARDENBURG, L. (2021). <i>Behind the scenes of artificial intelligence: Studying how organizations cope with machine learning in practice</i>. HAVEKA."
"authors" => array:1 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2023-09-11 17:33:22"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Livres"
"en" => "Books"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => "Pays-Bas"
"en" => "Netherlands"
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
6 => Essec\Faculty\Model\Contribution {#2207
#_index: "academ_contributions"
#_id: "14307"
#_source: array:18 [
"id" => "14307"
"slug" => "s-l-i-m-managen-van-ai-in-de-praktijk-hoe-organisaties-slimme-technologie-implementeren"
"yearMonth" => "2021-10"
"year" => "2021"
"title" => "S.L.I.M. managen van AI in de praktijk: Hoe organisaties slimme technologie implementeren"
"description" => "WAARDENBURG, L., HUYSMAN, M. et AGTERBERG, M. (2021). <i>S.L.I.M. managen van AI in de praktijk: Hoe organisaties slimme technologie implementeren</i>. Mediawerf."
"authors" => array:3 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
1 => array:1 [
"name" => "HUYSMAN Marleen"
]
2 => array:1 [
"name" => "AGTERBERG Marlous"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2023-09-11 17:33:30"
"publicationUrl" => "https://www.managementboek.nl/boek/9789490463809/slim-managen-van-ai-in-de-praktijk-lauren-waardenburg"
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Livres"
"en" => "Books"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
7 => Essec\Faculty\Model\Contribution {#2210
#_index: "academ_contributions"
#_id: "14309"
#_source: array:18 [
"id" => "14309"
"slug" => "hotspots-and-blind-spots"
"yearMonth" => "2018-12"
"year" => "2018"
"title" => "Hotspots and blind spots"
"description" => "WAARDENBURG, L., SERGEEVA, A. et HUYSMAN, M. (2018). Hotspots and blind spots. Dans: Schultze, U., Aanestad, M., Mähring, M., Østerlund, C., Riemer, K. eds. <i>Living with monsters? Social implications of algorithmic phenomena, hybrid agency, and the performativity of technology</i>. 1st ed. Cham: Springer, pp. 96–109."
"authors" => array:3 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
1 => array:1 [
"name" => "SERGEEVA Anastasia"
]
2 => array:1 [
"name" => "HUYSMAN Marleen"
]
]
"ouvrage" => "Living with monsters? Social implications of algorithmic phenomena, hybrid agency, and the performativity of technology"
"keywords" => array:3 [
0 => "analytics"
1 => "algorithms"
2 => "predictive policing"
]
"updatedAt" => "2024-01-19 12:23:47"
"publicationUrl" => "https://link.springer.com/chapter/10.1007/978-3-030-04091-8_8"
"publicationInfo" => array:3 [
"pages" => "96–109"
"volume" => "543"
"number" => ""
]
"type" => array:2 [
"fr" => "Chapitres"
"en" => "Book chapters"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "This paper reports on an ethnographic study of the use of analytics in police work. We find that the introduction of predictive policing was followed by the emergence of the new occupational role of “intelligence officer”. While intelligence officers were initially intended to merely support police officers by making sense of algorithmic outputs, they became increasingly influential in steering police action based on their judgments. Paradoxically, despite the largely subjective nature of intelligence officers’ recommendations, police officers started to increasingly believe in the superiority and objectivity of algorithmic decision-making. Our work contributes to the literature on occupational change and technology by highlighting how analytics can occasion the emergence of intermediary occupational roles. We argue that amidst critical debates on subjectivity of analytics, more attention should be paid to intermediaries – those who are in-between designers and users – who may exert the most consequential influence on analytics outcomes by further black-boxing the inherent inclusion of human expertise in analytics."
"en" => "This paper reports on an ethnographic study of the use of analytics in police work. We find that the introduction of predictive policing was followed by the emergence of the new occupational role of “intelligence officer”. While intelligence officers were initially intended to merely support police officers by making sense of algorithmic outputs, they became increasingly influential in steering police action based on their judgments. Paradoxically, despite the largely subjective nature of intelligence officers’ recommendations, police officers started to increasingly believe in the superiority and objectivity of algorithmic decision-making. Our work contributes to the literature on occupational change and technology by highlighting how analytics can occasion the emergence of intermediary occupational roles. We argue that amidst critical debates on subjectivity of analytics, more attention should be paid to intermediaries – those who are in-between designers and users – who may exert the most consequential influence on analytics outcomes by further black-boxing the inherent inclusion of human expertise in analytics."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
8 => Essec\Faculty\Model\Contribution {#2211
#_index: "academ_contributions"
#_id: "14310"
#_source: array:18 [
"id" => "14310"
"slug" => "juggling-street-work-and-data-work-an-ethnography-of-policing-and-reporting-practices"
"yearMonth" => "2022-08"
"year" => "2022"
"title" => "Juggling street work and data work: An ethnography of policing and reporting practices"
"description" => "WAARDENBURG, L., SERGEEVA, A. et HUYSMAN, M. (2022). Juggling street work and data work: An ethnography of policing and reporting practices. Dans: <i>82nd Annual Meeting of the Academy of Management</i>. Seattle: Academy of Management."
"authors" => array:3 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
1 => array:1 [
"name" => "SERGEEVA Anastasia"
]
2 => array:1 [
"name" => "HUYSMAN Marleen"
]
]
"ouvrage" => "82nd Annual Meeting of the Academy of Management"
"keywords" => []
"updatedAt" => "2023-09-12 01:00:39"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
9 => Essec\Faculty\Model\Contribution {#2212
#_index: "academ_contributions"
#_id: "14311"
#_source: array:18 [
"id" => "14311"
"slug" => "what-are-we-augmenting-a-multidisciplinary-analysis-of-ai-based-augmentation-for-the-future-of-work"
"yearMonth" => "2022-12"
"year" => "2022"
"title" => "What are we augmenting? A multidisciplinary analysis of AI-based augmentation for the future of work"
"description" => "BAER, I., WAARDENBURG, L. et HUYSMAN, M. (2022). What are we augmenting? A multidisciplinary analysis of AI-based augmentation for the future of work. Dans: <i>ICIS 2022</i>. Copenhagen: ICIS."
"authors" => array:3 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
1 => array:1 [
"name" => "BAER Inès"
]
2 => array:1 [
"name" => "HUYSMAN Marleen"
]
]
"ouvrage" => "ICIS 2022"
"keywords" => []
"updatedAt" => "2023-09-12 01:00:39"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
10 => Essec\Faculty\Model\Contribution {#2213
#_index: "academ_contributions"
#_id: "14313"
#_source: array:18 [
"id" => "14313"
"slug" => "de-situated-police-officers-the-embodied-and-material-realities-of-accessing-information-in-action"
"yearMonth" => "2022-06"
"year" => "2022"
"title" => "De-situated police officers: The embodied and material realities of accessing information in action"
"description" => "WAARDENBURG, L. et HAFERMALZ, E. (2022). De-situated police officers: The embodied and material realities of accessing information in action. Dans: <i>13th PROS Symposium</i>. Rhodes."
"authors" => array:2 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
1 => array:1 [
"name" => "HAFERMALZ Ella"
]
]
"ouvrage" => "13th PROS Symposium"
"keywords" => []
"updatedAt" => "2023-09-12 01:00:39"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
11 => Essec\Faculty\Model\Contribution {#2214
#_index: "academ_contributions"
#_id: "14314"
#_source: array:18 [
"id" => "14314"
"slug" => "the-burden-of-data-production-how-anticipating-data-work-shapes-police-practices"
"yearMonth" => "2021-07"
"year" => "2021"
"title" => "The burden of data production: How anticipating data work shapes police practices"
"description" => "WAARDENBURG, L., SERGEEVA, A. et HUYSMAN, M. (2021). The burden of data production: How anticipating data work shapes police practices. Dans: <i>37th Colloquium of the European Group for Organizational Studies</i>. Amsterdam."
"authors" => array:3 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
1 => array:1 [
"name" => "SERGEEVA Anastasia"
]
2 => array:1 [
"name" => "HUYSMAN Marleen"
]
]
"ouvrage" => "37th Colloquium of the European Group for Organizational Studies"
"keywords" => []
"updatedAt" => "2023-09-12 01:00:39"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
12 => Essec\Faculty\Model\Contribution {#2215
#_index: "academ_contributions"
#_id: "14315"
#_source: array:18 [
"id" => "14315"
"slug" => "filling-the-void-how-occupational-authority-emerges-from-curating-learning-algorithms"
"yearMonth" => "2020-08"
"year" => "2020"
"title" => "Filling the void: How occupational authority emerges from curating learning algorithms"
"description" => "WAARDENBURG, L. (2020). Filling the void: How occupational authority emerges from curating learning algorithms. Dans: <i>80th Annual Meeting of the Academy of Management</i>. Online."
"authors" => array:1 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
]
"ouvrage" => "80th Annual Meeting of the Academy of Management"
"keywords" => []
"updatedAt" => "2023-09-12 01:00:39"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-05-07T01:21:56.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 6.7039223
+"parent": null
}
13 => Essec\Faculty\Model\Contribution {#2216
#_index: "academ_contributions"
#_id: "14754"
#_source: array:18 [
"id" => "14754"
"slug" => "chapter-12-it-takes-a-village-the-ecology-of-explaining-ai"
"yearMonth" => "2024-03"
"year" => "2024"
"title" => "Chapter 12: It takes a village: the ecology of explaining AI"
"description" => "WAARDENBURG, L. et MÁRTON, A. (2024). Chapter 12: It takes a village: the ecology of explaining AI. Dans: Ioanna Constantiou, Mayur P. Joshi, Marta Stelmaszak eds. <i>Research Handbook on Artificial Intelligence and Decision Making in Organizations</i>. 1st ed. Edward Elgar Publishing Ltd, pp. 214–225."
"authors" => array:2 [
0 => array:3 [
"name" => "WAARDENBURG Lauren"
"bid" => "B00812212"
"slug" => "waardenburg-lauren"
]
1 => array:1 [
"name" => "MÁRTON Attila"
]
]
"ouvrage" => "Research Handbook on Artificial Intelligence and Decision Making in Organizations"
"keywords" => array:6 [
0 => "Artificial intelligence"
1 => "Decision-making"
2 => "Explainable AI"
3 => "Predictive policing"
4 => "Ecology"
5 => "Decision-paradox"
]
"updatedAt" => "2024-03-18 12:22:11"
"publicationUrl" => "https://doi.org/10.4337/9781803926216.00021"
"publicationInfo" => array:3 [
"pages" => "214–225"
"volume" => "Business 2024"
"number" => "12"
]
"type" => array:2 [
"fr" => "Chapitres"
"en" => "Book chapters"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "AI systems are commonly believed to be able to aid in more objective decision-making and, eventually, to make objective decisions of their own. However, such belief is riddled with fallacies, which are based on an overly simplistic approach to organizational decision-making. Based on an ethnography of the Dutch police, we demonstrate that making decisions with AI requires practical explanations that go beyond an analysis of the computational methods used to generate predictions, to include an entire ecology of unbounded, open-ended interactions and interdependencies. In other words, explaining AI is ecological. Yet, this typically goes unnoticed. We argue that this is highly problematic, as it is through acknowledging this ecology that we can recognize that we are not, and never will be, making objective decisions with AI. If we continue to ignore the ecology of explaining AI, we end up reinforcing, and potentially even further stigmatizing, existing societal categories."
"en" => "AI systems are commonly believed to be able to aid in more objective decision-making and, eventually, to make objective decisions of their own. However, such belief is riddled with fallacies, which are based on an overly simplistic approach to organizational decision-making. Based on an ethnography of the Dutch police, we demonstrate that making decisions with AI requires practical explanations that go beyond an analysis of the computational methods used to generate predictions, to include an entire ecology of unbounded, open-ended interactions and interdependencies. In other words, explaining AI is ecological. Yet, this typically goes unnoticed. We argue that this is highly problematic, as it is through acknowledging this ecology that we can recognize that we are not, and never will be, making objective decisions with AI. If we continue to ignore the ecology of explaining AI, we end up reinforcing, and potentially even further stigmatizing, existing societal categories."
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14 => Essec\Faculty\Model\Contribution {#2217
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15 => Essec\Faculty\Model\Contribution {#2218
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16 => Essec\Faculty\Model\Contribution {#2219
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