{"title":"从共存到共同创造:人工智能时代的边界模糊","authors":"Lauren Waardenburg , Marleen Huysman","doi":"10.1016/j.infoandorg.2022.100432","DOIUrl":null,"url":null,"abstract":"<div><p><span>While the self-learning nature of AI systems that use </span>machine learning<span><span> 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, </span>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.</span></p></div>","PeriodicalId":47253,"journal":{"name":"Information and Organization","volume":"32 4","pages":"Article 100432"},"PeriodicalIF":5.7000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"From coexistence to co-creation: Blurring boundaries in the age of AI\",\"authors\":\"Lauren Waardenburg , Marleen Huysman\",\"doi\":\"10.1016/j.infoandorg.2022.100432\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>While the self-learning nature of AI systems that use </span>machine learning<span><span> 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, </span>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.</span></p></div>\",\"PeriodicalId\":47253,\"journal\":{\"name\":\"Information and Organization\",\"volume\":\"32 4\",\"pages\":\"Article 100432\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information and Organization\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1471772722000458\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Organization","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1471772722000458","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
From coexistence to co-creation: Blurring boundaries in the age of AI
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.
期刊介绍:
Advances in information and communication technologies are associated with a wide and increasing range of social consequences, which are experienced by individuals, work groups, organizations, interorganizational networks, and societies at large. Information technologies are implicated in all industries and in public as well as private enterprises. Understanding the relationships between information technologies and social organization is an increasingly important and urgent social and scholarly concern in many disciplinary fields.Information and Organization seeks to publish original scholarly articles on the relationships between information technologies and social organization. It seeks a scholarly understanding that is based on empirical research and relevant theory.