Special issue of <i>Production and Operations Management</i> on “Responsible Data Science”
Notice bibliographique
Résumé
Submissions open: August 1, 2022 Deadline: November 30, 2022 Our society is experiencing a rapid digital transformation, changing the way how different players in supply chains and technological systems interact with each other and exert their influences. For example, the way that businesses and customers interact has changed in the digital economy with the influence of computing technology and information sharing. Businesses now routinely collect large volumes of fine-grained data to analyze consumers’ behavior, and consumers can also track changes in firms’ strategies to make informed purchasing decisions. An iconic trend in the era of digital transformation is the increasingly extensive use of data analytics and machine learning tools in decision making as both strategic and operational levers. The use of rich and large data sets also raises critical societal concerns. For example, data sets often include personal sensitive information that can be exploited, without explicit knowledge and/or consent from the involved individuals, for various purposes including monitoring, discrimination, and illegal activities. On the one hand, data- and artificial intelligence (AI)-driven algorithms may have created a competitive advantage for firms that are using these algorithms. For example, large corporations can create unequal competition in the market against smaller players. Similarly, firms may attract customers with stronger financial records by offering personalized enticing incentives, leading to a social bias toward individuals who are offered fewer appealing opportunities. On the other hand, algorithms that do consider social inclusion and fairness considerations have a great potential to reduce the inequalities induced by social status, gender, and race, just to name a few. Responsible data science is defined as the utilization and exploitation of data via manual analysis or automated algorithms (such as machine learning) that aim at improving the terms of participation in society, particularly for individuals or corporate entities that are disadvantaged. Such societal participation improvements include, but are not limited to, enhanced opportunities, increased access to resources, and greater voice and respect for human rights. This special issue aims to attracting submissions that are closely connected to real-world operational problems and have the potential to impact practice from the lens of responsible data science. All submissions must have clear managerial or theoretical contributions, and must be built upon rigorous research methods that serve as an appropriate framework to analyze problems: including analytical modeling, econometric analysis, field experimentation, and behavioral theories. Papers should be submitted through the POM manuscript central website: https://mc.manuscriptcentral.com/poms. Specifically, please follow the prompts below: On the author tab, please choose “Special Issue Article” (see the image below) in Step 1 In the drop-down menu (see the image below) that then appears in Step 1, please select appropriate department editor: Special Issue on Responsible Data Science. For Step 6, please upload a cover letter that includes the title of the special issue and the specific article type you are submitting. Towards the end of Step 6, please indicate “yes” for the question “Is this submission for a special issue?” and enter the title of the special issue in the text box below: “Responsible Data Science.”
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».