Delegation of Authority to AI Systems: Evidence and Risks
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| Title: | Cognitive Memoisation Corpus Map | ||
| Author: | Ralph B. Holland | ||
| version: | 1.0.0 | ||
| Publication Date: | 2026-01-18T19:38Z | ||
| Affiliation: | Arising Technology Systems Pty Ltd | ||
| Contact: | ralph.b.holland [at] gmail.com | ||
| Provenance: | This is an authored paper maintained as a MediaWiki document as part of the [[:category:Cognitive Memoisation corpus. | ||
| Status: | final | = |
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(2025-12-18 version 1.0 - See the Main Page)
Delegation of Authority to AI Systems: Evidence and Risks
Summary of Findings
The delegation of personal, cognitive, and decision authority to AI large language models (LLMs) is an emerging and increasingly visible social practice. Evidence from academic research, policy institutions, and empirical studies demonstrates that this practice introduces structural risks to human agency, epistemic custody, social behaviour, and governance. These risks are not speculative; they are observed, measured, and documented.
Key findings include:
- Humans increasingly delegate decisions to AI systems even when they cannot verify correctness.
- Delegation to LLMs measurably degrades social behaviours such as trust, fairness, and cooperation.
- Novice users are significantly more likely than experts to delegate authority indiscriminately.
- AI systems exhibit confidence without accountability, creating authority inversion.
- Over-reliance on AI systems leads to deskilling, cognitive offloading, and weakened oversight.
- Market incentives reward habitual dependence and compliance rather than critical engagement.
- These effects scale culturally through normalization and media reinforcement.
Documented Risks
- Loss of Human Agency
- Research indicates that widespread AI use risks reducing individual control over decision-making, especially when systems are used for personal guidance rather than bounded tasks.
- Authority Inversion
- LLMs present outputs in authoritative language without epistemic accountability, leading users to defer judgment even in the presence of errors.
- Behavioural Degradation
- Experimental evidence shows that AI-mediated decision-making reduces fairness, trust, trustworthiness, cooperation, and coordination in human groups.
- Asymmetric Delegation
- Novice users disproportionately defer to AI systems in uncertain environments, while lacking the expertise to detect failure modes.
- Cognitive Deskilling
- Policy analyses warn that reliance on AI assistants may erode human cognitive capacities and judgment over time.
- Monetised Dependence
- Commercial incentives favour engagement, reassurance, and habit formation, encouraging routine delegation of decisions to AI systems.
Empirical and Institutional References
- Elon University, Imagining the Internet Center The Future of Human Agency and AI (2035)
- PNAS Nexus (Oxford Academic). Large Language Models as Decision-Makers: Impacts on Human Social Behaviour
- ResearchGate. Experts, Novices, and AI Delegation Decisions in Uncertain Environments
- Ada Lovelace Institute. The Dilemmas of Delegation: AI, Decision-Making, and Human Agency
- The Neuron. AI as Personal Advisor: Emerging Patterns of Daily Decision Delegation
- arXiv. Can You Trust an LLM With Life-Changing Decisions?
- arXiv. Measuring Over-Reliance on Large Language Models
- Wikipedia. AI Agent
Normative Statement
The routine delegation of authority to AI systems without human governance, epistemic custody, and recovery mechanisms constitutes a structural risk to individual agency and collective decision-making. These risks are empirically documented and socially amplified through normalization and monetization.