Shaokun Fan , Noyan Ilk , Akhil Kumar , Ruiyun Xu , J. Leon Zhao
{"title":"Blockchain as a trust machine: From disillusionment to enlightenment in the era of generative AI","authors":"Shaokun Fan , Noyan Ilk , Akhil Kumar , Ruiyun Xu , J. Leon Zhao","doi":"10.1016/j.dss.2024.114251","DOIUrl":null,"url":null,"abstract":"<div><p>Since the Economist magazine heralded blockchain as “the trust machine” in 2015, the blockchain paradigm has experienced crests and falls, including a recent phase of disillusionment due to its failure to meet the high expectations, e.g., to revolutionize record keeping, data management, and workflow, envisioned during its early history. However, despite the waning interest in this technology in some quarters, its deployment has become ever more essential in areas such as decentralized finance (DeFi), Non-fungible Tokens (NFTs), and other application domains beyond cryptocurrencies. In particular, recent advancements in Artificial Intelligence (AI) surrounding Large Language Models (LLM) offer new opportunities for blockchain adoption where trust and reliability become critical. As the blockchain technology transitions from a stage of disillusionment to one of enlightenment, anticipation is building for its mainstream adoption, with focused endeavors towards removing adoption barriers across diverse business contexts, exemplified by studies included in this special issue on <em>Blockchain Technology and Applications</em>. In this paper, we first survey the current state of the blockchain technology and then highlight its potential for enhancing trust and accountability in emerging phenomena such as AI generated content (AIGC). We conclude by introducing the papers included in the special issue.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"182 ","pages":"Article 114251"},"PeriodicalIF":6.7000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Support Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167923624000848","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Since the Economist magazine heralded blockchain as “the trust machine” in 2015, the blockchain paradigm has experienced crests and falls, including a recent phase of disillusionment due to its failure to meet the high expectations, e.g., to revolutionize record keeping, data management, and workflow, envisioned during its early history. However, despite the waning interest in this technology in some quarters, its deployment has become ever more essential in areas such as decentralized finance (DeFi), Non-fungible Tokens (NFTs), and other application domains beyond cryptocurrencies. In particular, recent advancements in Artificial Intelligence (AI) surrounding Large Language Models (LLM) offer new opportunities for blockchain adoption where trust and reliability become critical. As the blockchain technology transitions from a stage of disillusionment to one of enlightenment, anticipation is building for its mainstream adoption, with focused endeavors towards removing adoption barriers across diverse business contexts, exemplified by studies included in this special issue on Blockchain Technology and Applications. In this paper, we first survey the current state of the blockchain technology and then highlight its potential for enhancing trust and accountability in emerging phenomena such as AI generated content (AIGC). We conclude by introducing the papers included in the special issue.
期刊介绍:
The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).