Dana Cupkova, A. Wit, Matias del Campo, Mollie Claypool
{"title":"AI, architecture, accessibility, and data justice—ACADIA special issue","authors":"Dana Cupkova, A. Wit, Matias del Campo, Mollie Claypool","doi":"10.1177/14780771231171939","DOIUrl":null,"url":null,"abstract":"In recent years, the field of architectural research has trended towards rapid evolution as new digital technologies that integrate artificial intelligence (AI) into design, representation, and production have become more prominent. As with any paradigm shift and rapid emergence of transformative technology, new tensions and fears of human distancing away from acts of design and making arise. Outside of architecture, AI already plays a significant role in fields such as engineering, IT, and the social/political sciences, with a deepening discourse on its effect on humanity, and the ethics of its labor. Architects must develop critical metrics, understand implicit biases, and probe new methodologies to better understand the impacts and implications these transformative technologies have within their own territory. It is now more urgent than ever for architecture to take a stance on shaping the agency of AI frameworks within the discipline. Traditionally, advances in architectural technologies were limited in access due to the high monetary costs and steep learning curves in the physical infrastructure and tools utilized in digital fabrication and robotic production. However, recent breakthroughs in AI technologies have seemed to enable the digital networks provided by AI to be increasingly distributed to those already abled by technological access. As a result of this paradigm shift, new models of economy and labor arise, and the use of AI yet again opens questions surrounding the role of authorship, ownership of data, and models of collaboration within the discipline. In this new era of increased AI ubiquity and seemingly rapid design freedom aided by machine learning (ML) frameworks, a series of critical questions emerge through the articles curated in this volume:","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"209 - 210"},"PeriodicalIF":1.6000,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Architectural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/14780771231171939","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, the field of architectural research has trended towards rapid evolution as new digital technologies that integrate artificial intelligence (AI) into design, representation, and production have become more prominent. As with any paradigm shift and rapid emergence of transformative technology, new tensions and fears of human distancing away from acts of design and making arise. Outside of architecture, AI already plays a significant role in fields such as engineering, IT, and the social/political sciences, with a deepening discourse on its effect on humanity, and the ethics of its labor. Architects must develop critical metrics, understand implicit biases, and probe new methodologies to better understand the impacts and implications these transformative technologies have within their own territory. It is now more urgent than ever for architecture to take a stance on shaping the agency of AI frameworks within the discipline. Traditionally, advances in architectural technologies were limited in access due to the high monetary costs and steep learning curves in the physical infrastructure and tools utilized in digital fabrication and robotic production. However, recent breakthroughs in AI technologies have seemed to enable the digital networks provided by AI to be increasingly distributed to those already abled by technological access. As a result of this paradigm shift, new models of economy and labor arise, and the use of AI yet again opens questions surrounding the role of authorship, ownership of data, and models of collaboration within the discipline. In this new era of increased AI ubiquity and seemingly rapid design freedom aided by machine learning (ML) frameworks, a series of critical questions emerge through the articles curated in this volume: