Blockchain as a trust machine: From disillusionment to enlightenment in the era of generative AI

被引:4
|
作者
Fan, Shaokun [1 ]
Ilk, Noyan [2 ]
Kumar, Akhil [3 ]
Xu, Ruiyun [4 ]
Zhao, J. Leon [5 ]
机构
[1] Oregon State Univ, Coll Business, Corvallis, OR USA
[2] Florida State Univ, Coll Business, Dept Business Analyt Informat Syst & Supply Chain, Tallahassee, FL USA
[3] Penn State Univ, Smeal Coll Business, Dept Supply Chain & Informat Syst, University Pk, PA USA
[4] Miami Univ, Farmer Sch Business, Dept Informat Syst & Analyt, Oxford, OH USA
[5] Chinese Univ Hong Kong, Shenzhen Finance Inst, Sch Management & Econ, Shenzhen, Peoples R China
基金
中国国家自然科学基金;
关键词
Blockchain; Decentralized finance; Research directions; Trust; Accountability; CHALLENGES;
D O I
10.1016/j.dss.2024.114251
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页数:8
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