Perceiving AI as an Epistemic Authority or Algority: A User Study on the Human Attribution of Authority to AI
Open accessAbstract
The increasing integration of artificial intelligence (AI) in decision-making processes has amplified discussions surrounding algorithmic authority—the perceived epistemic legitimacy of AI systems over human judgment. This study investigates how individuals attribute epistemic authority to AI, focusing on psychological, contextual, and sociotechnical factors. Existing research highlights the importance of trust in automation, perceived performance, and moral frameworks in shaping such attributions. Unlike prior conceptual or philosophical accounts of algorithmic authority, our study adopts a relational and empirically grounded perspective by operationalizing algority through psychometric measures and contextual assessments. To address knowledge gaps in the micro-level dynamics of this phenomenon, we conducted an empirical study using psychometric tools and scenario-based assessments. Here, we report key findings from a survey of 610 participants, revealing significant correlations between trust in automation (TiA), perceptions of automated performance (PAS), and the propensity to defer to AI, particularly in high-stakes scenarios like criminal justice and job-matching. Trust in automation emerged as a primary factor, while moral attitudes moderated deference in ethically sensitive contexts. Our findings highlight the practical relevance of transparency and explainability for supporting critical engagement with AI outputs and for informing the design of contextually appropriate decision support. This study contributes to understanding algorithmic authority as a multidimensional construct, offering empirically grounded insights for designing AI systems that are trustworthy and context-sensitive.
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Users increasingly attribute epistemic authority to generative AI despite limited awareness of its methodological constraints, uncertainty, and embedded biases.
“Recent empirical research indicates that users increasingly attribute epistemic authority to generative AI despite limited awareness of its methodological constraints, uncertainty, and embedded biases [25].”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [25]
Section 5.1 reports that trust and perceived AI performance influence participants’ readiness to accept AI as an epistemic authority. But the study does not establish an increasing trend, focus its finding on generative AI, or show that users have limited awareness of methodological constraints, uncertainty, and embedded biases. Section 6 instead calls for future research on how trust changes over time. (LLM, conf=0.86)
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- Authors
- Milella, F., & Cabitza, F.
- Affiliations
- University of Milano-Bicocca (IT)
- Fondazione Bruno Kessler (IT)
- Corresponding author
- Milella, Frida
- Venue
- Machine Learning and Knowledge Extraction
- Publisher
- MDPI AG
- Published
- 2026-02-05
- Volume
- 8
- Issue
- 2
- Pages
- 36
- ISSN
- 2504-4990 (ISSN-L)
- OpenAlex
- W7128034622
- Cited by
- 11
- References
- 57
- Citations by year
- 2026: 11
- FWCI
- 61.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Machine Learning and Knowledge Extraction (publishedVersion, cc-by)
- BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- European Union—Next Generation EU (AGE-IT-PE00000015 CUP: H43C22000840006)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic authority, epistemic authority, trust in automation, moral attitudes, high-stakes decision making, criminal justice, job matching, AI transparency, explainability, decision support, trustworthy AI
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