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Algorithmic Authority: The Case of Bitcoin
Open accessAbstract
In this paper, we propose a new concept for understanding the role of algorithms in daily life: algorithmic authority. Algorithmic authority is the legitimate power of algorithms to direct human action and to impact which information is considered true. We use this concept to examine the culture of users of Bit coin, a crypto-currency and payment platform. Through Bit coin, we explore what it means to trust in algorithms. Our study utilizes interview and survey data. We found that Bit coin users prefer algorithmic authority to the authority of conventional institutions, which they see as untrustworthy. However, we argue that Bit coin users do not have blind faith in algorithms, rather, they acknowledge the need for mediating algorithmic authority with human judgment. We examine the tension between members of the Bit coin community who would prefer to integrate Bit coin with existing institutions and those who would prefer to resist integration.
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“As a result, people often regard technologically-run institutions to be more 'trustworthy' than human-led institutions [20], which, as the article will argue, is in fact the capacity of technology to build confidence.”
From Blockchain as a confidence machine: The problem of trust & challenges of governance (2020) · cites this paper as [20]
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“Lustig and Nardi [26] pose special attention onto algorithms as defining aspect of Bitcoin, whereas we argue that ledger's maintainers and tokens' users are what characterize the governance of blockchain as much as the other governance purposes it can be used for.”
From Tribal Governance: The Business of Blockchain Authentication (2018) · cites this paper as [26]
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“Having confidence in the system ultimately means trusting the whole assemblage of actors associated with that network [20].”
From Blockchain as a confidence machine: The problem of trust & challenges of governance (2020) · cites this paper as [20]
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“Accordingly, despite the decentralized nature of a blockchain-based system, some level of oversight is nonetheless necessary in order to ensure the proper functioning of the network [20].”
From Blockchain as a confidence machine: The problem of trust & challenges of governance (2020) · cites this paper as [20]
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Metadata
- Authors
- Lustig, C., & Nardi, B.
- Affiliations
- University of California, Irvine (US)
- Venue
- 2015 48th Hawaii International Conference on System Sciences
- Publisher
- IEEE
- Published
- 2015-01
- Pages
- 743-752
- ISSN
- 1530-1605 (ISSN-L)
- OpenAlex
- W1558585115
- Cited by
- 166
- References
- 41
- Citations by year
- 2026: 12 · 2025: 11 · 2024: 18 · 2023: 11 · 2022: 17 · 2021: 19 · 2020: 26 · 2019: 22 · 2018: 9 · 2017: 12 · 2016: 7 · 2015: 2
- FWCI
- 47.95
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- cc-by-sa
- Locations
- Hawaii International Conference on System Sciences (HICSS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- algorithmic authority, Bitcoin, cryptocurrency users, trust in algorithms, institutional authority, human judgment, algorithmic governance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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