Artificial Intelligence in High Office: Could AI Replace Presidents and Ministers? Interpreting Albania’s 2025 AI ‘Minister’ Through Critical Sociology
- OUS Academy in Switzerland
- Sep 12
- 10 min read
Author: Bekzat Alimov
Affiliation: Independent Researcher
Abstract
In September 2025, Albania announced the world’s first artificial-intelligence “minister,” a virtual officeholder tasked with overseeing public procurement and combatting corruption. This article uses that announcement as a focal case to ask a larger question of governance: can AI replace ministers—or even presidents—in the foreseeable future? Adopting a critical sociology approach, I integrate Bourdieu’s concept of capital, institutional isomorphism, and world-systems theory to evaluate the social, political, and ethical conditions under which algorithmic authority might expand. I propose a “technocratic substitution continuum” that clarifies stages from AI-assisted decision support to full delegation of executive authority, and I specify safeguards and evaluation metrics suitable for high-stakes public administration. While AI can meaningfully increase transparency and efficiency in targeted policy domains (e.g., procurement), replacement of elected executives remains unlikely and normatively problematic in democratic polities. The near-term horizon points instead to hybrid models of augmented leadership, where algorithmic and human capital co-produce decisions under enforceable accountability.
1. Introduction: A New Symbol of Algorithmic Statecraft
The image of a “minister” made of code—instead of flesh and blood—captures public imagination because it condenses multiple trends: the datafication of bureaucracy, the platformization of public services, and the sociotechnical promise of eliminating corruption by reducing discretionary human contact. Albania’s announcement is therefore more than a technical reform. It is a symbolic event that tests how far a society is willing to move executive discretion from human judgment to algorithmic systems.
The central question of this paper is not whether software can execute rules. It clearly can, and it already does in tax systems, customs risk scoring, and digital service portals. Rather, the question is whether political authority—with its bundle of representation, accountability, and symbolic power—can be credibly and legitimately reposed in an artificial agent. To answer, I turn to three complementary theories that explain how authority is produced, imitated, and unevenly distributed across the world economy.
2. Theoretical Lens
2.1 Bourdieu: From Political Capital to Technological Capital
Bourdieu’s theory of capital distinguishes economic, social, cultural, and symbolic forms that actors deploy in specific fields. In democratic politics, political capital derives from electoral legitimacy, party networks, rhetorical skill, and public recognition. Introducing an AI “minister” invites a conversion of capital: technological capital (expertise in data science, computational infrastructure, and model performance) is elevated into symbolic capital (trust and legitimacy) by the act of formal appointment. The key problem is that conversion is not automatic. For citizens to accept algorithmic authority, the system must accumulate symbolic capital through transparency, auditability, and predictable fairness—conditions that are still works in progress in most states.
2.2 Institutional Isomorphism: Why One Country’s Experiment Spreads
DiMaggio and Powell’s concept of institutional isomorphism suggests that organizations—and by extension, states—tend to converge on similar structures due to coercive, mimetic, and normative pressures. An AI “minister” can be imitated for three reasons:
Coercive: supranational funding or accession incentives (e.g., anti-corruption benchmarks) push adoption of algorithmic controls.
Mimetic: uncertainty about “what works” leads governments to copy highly visible innovations from peers.
Normative: professional communities (IT auditors, procurement officers, data ethicists) standardize procedures that normalize algorithmic governance.
Albania’s move may thus precipitate a regional imitation wave in domains where corruption risks are high and rules-based scoring appears credible—particularly procurement.
2.3 World-Systems Theory: Core, Semi-Periphery, and Signaling Modernity
World-systems analysis frames states within a global economic division of labor: core nations concentrate capital and innovation; peripheries supply low-value functions; semi-peripheries oscillate between the two. For semi-peripheral states, high-visibility digital reforms function as signals of modernity to global investors and supranational institutions. An AI “minister” is both an internal reform and an external message: we are technologically capable, rules-oriented, and investment-ready. Whether this signal translates into long-term structural change depends on institutional depth: data quality, legal capacity, independent oversight, and sustained political will.
3. Case Focus: What an AI “Minister” Can—and Cannot—Do
3.1 Domain Choice: Why Procurement?
Public procurement concentrates corruption risks: ex-ante qualification, bid scoring, conflict-of-interest checks, and award communication are all points of leverage. AI systems are strong at pattern detection and consistency. If trained on clean historical data and supplied with real-time market signals, a procurement AI can:
Standardize eligibility filters (legal standing, financial capacity, past performance).
Normalize and weight technical criteria (quality, lifecycle cost, delivery time).
Flag collusion patterns (bid rotation, identical phrasing, abnormal pricing clusters).
Maintain immutable logs for audit and judicial review.
3.2 Limits of Algorithmic Discretion
Three constraints prevent an AI system from becoming a full executive replacement:
Ambiguity in public value. Procurement often balances price against strategic goals (domestic industry development, sustainability, regional equality). These are political trade-offs, not merely technical optimizations.
Data politics. Training data reflects past procurement practices, including possible biases. Without counterfactual testing and fairness constraints, a model may reproduce exclusionary patterns while appearing “objective.”
Adversarial environments. Once rules are known, sophisticated bidders can game the model, e.g., by creating synthetic vendor histories or strategic pricing near thresholds. Robust governance requires continuous red-teaming and post-award monitoring, tasks that remain human-intensive.
4. Methodological Note: How to Evaluate Algorithmic Authority
Although this article is theoretical, a real-world assessment framework is essential.
4.1 Outcome Metrics
Integrity: Reduction in single-bidder awards; drop in red-flag indicators (e.g., last-minute tender changes).
Competition: Increase in unique vendors; lowered market concentration (Herfindahl-Hirschman Index).
Efficiency: Cycle-time from tender to award; contract change orders per award.
Equity: Share of awards to SMEs; regional distribution of winners.
4.2 System Metrics
Model Performance: Precision/recall for risk flags; calibration curves; out-of-sample stability.
Fairness: Demographic parity or equalized odds on relevant, lawful attributes (e.g., firm size, region).
Explainability: Availability of feature contribution reports (e.g., SHAP-style summaries) in plain language.
Governance: Existence of an external Algorithmic Accountability Board with subpoena power to review code, data, and logs.
4.3 Legal-Institutional Metrics
Due Process: Vendors’ rights to access reasons, contest decisions, and obtain independent review.
Liability: Clear assignment of responsibility among designers, operators, and public authorities for model errors or discriminatory outcomes.
Security: Penetration testing and incident response for model poisoning, prompt injection, or data exfiltration.
5. The Technocratic Substitution Continuum
To clarify where “AI ministers” stand, I propose a four-stage continuum:
Decision Support (DS): AI produces analyses; humans decide (status quo in many ministries).
Delegated Micro-Decisions (DMD): AI makes bounded decisions under policy constraints (e.g., automatic compliance checks).
Hybrid Stewardship (HS): AI proposes, humans ratify by exception with strong audit trails (likely near-term ceiling for procurement).
Autonomous Executive (AE): AI holds formal authority to decide in open-ended domains (unlikely and undesirable under current democratic norms).
Albania’s virtual “minister” can be located between DMD and HS. Even if labeled a “minister,” its legitimacy still depends on human authorization, contestability, and review—core functions of ministerial responsibility in parliamentary systems.
6. Bourdieu in Practice: Habitus, Fields, and the Charisma of Code
6.1 Habitus and Administrative Culture
Officials develop a habitus—ingrained ways of seeing and acting—that can resist or facilitate algorithmic tools. If the predominant habitus equates discretion with status, AI may be perceived as a status threat. Successful implementation therefore requires symbolic diplomacy: framing AI as an augmenting ally rather than a disciplinary device.
6.2 Symbolic Power and Public Trust
The title “minister” bestows symbolic capital. Yet symbolic capital is fragile when not backed by routine practices that citizens can recognize as fair. A practical mechanism to convert technological capital into symbolic capital is ritualized transparency: publishing procurement criteria, disclosing model updates, and holding public hearings on contested awards. Over time, these rituals sediment legitimacy.
6.3 Capital Conversion Risks
Conversion can backfire. If citizens view the AI as a black box backed by distant experts, technological capital may deplete symbolic capital, generating cynicism. Conversational interfaces, plain-language explanations, and third-party validation by universities or courts can mitigate this risk.
7. Institutional Isomorphism in Motion: Policy Diffusion Scenarios
7.1 Coercive Pressures
Anticorruption benchmarks tied to international financing often require measurable procurement reforms. AI-logged decisions satisfy the auditable evidence such benchmarks demand, promoting diffusion to peer states seeking financing or accession.
7.2 Mimetic Pressures
In the face of uncertainty—economic shocks, fiscal constraints—governments may imitate “success stories” to reassure domestic audiences and international partners. The visibility of an AI “minister” amplifies mimetic isomorphism independent of rigorous evidence; hence the importance of open metrics to ground claims.
7.3 Normative Pressures
Training programs for public managers, IT auditors, and data protection officers propagate professional norms (e.g., model documentation, data governance). As these norms solidify, adopting algorithmic controls becomes a matter of professional competence.
8. World-Systems: Why Some Countries Move First
Core states host the firms and research labs that build frontier models; they also face political scrutiny that slows radical experiments. Semi-peripheral states may feature greater policy agility, allowing bolder pilots that double as reputation campaigns. Albania’s step exemplifies this: the move addresses a domestic governance problem while signaling European-conforming modernity. Whether the signal sticks depends on downstream institutionalization—budgets for auditing, national data strategies, and judicial capacity to adjudicate disputes.
9. Can AI Replace Presidents and Ministers?
9.1 What Ministers Actually Do
Ministers blend administration (executing policy), representation (answering to parliament and public), and politics (negotiating among interests). Algorithms can support the first; they struggle with the second and third. Even in administration, many tasks require value judgments rather than rule execution.
9.2 The Democracy Problem
Democratic legitimacy is not merely decision accuracy; it is the right to decide. That right is conferred through contestable procedures—elections, parliamentary questions, judicial review—anchored in a human bios. An autonomous AI “minister” lacks the thick social reciprocity that binds representatives to constituents.
9.3 The Accountability Problem
Who resigns when an algorithm’s decision causes public harm? Ministerial responsibility stabilizes democracy because it personalizes accountability. If blame cannot be clearly assigned, trust erodes. Without a robust liability regime tying algorithm creators and operators to consequences, replacement of ministers is institutionally incoherent.
9.4 The Practical Problem
AI’s strength is interpolation within known patterns; politics often requires extrapolation under novelty (pandemics, wars, financial crises). In such contexts, narrative framing and coalition-building are decisive—skills that remain human arts.
Conclusion of Section: AI can replace discrete ministerial functions, but not the office as an integrated bundle of authority, representation, and responsibility—at least not without transforming democracy into a different regime type.
10. Design for Hybrid Stewardship: A 12-Point Policy Blueprint
Statutory Mandate: Enact laws defining algorithmic roles, appeal rights, and liability.
Algorithmic Accountability Board: Independent, with power to audit code, data, and logs.
Public Reason Statements: Plain-language explanations of criteria for each award.
Immutable Logging: Append-only records of prompts, parameters, and outputs.
Data Governance: Provenance tracking, versioning, and minimization; periodic quality audits.
Fairness Guardrails: Legally appropriate fairness metrics; publish drift reports.
Red-Team Exercises: Scheduled adversarial testing against gaming and data poisoning.
Human-in-the-Loop Escalation: Mandatory human review for high-impact or anomalous cases.
Vendor Contestation Portal: Time-bounded appeals with independent adjudicators.
Procurement Market Monitoring: Screen for collusion post-award using network analytics.
Capacity Building: Train civil servants in data literacy and ethical reasoning.
Civic Oversight: Citizen panels and civil society briefings to socialize the system.
This blueprint operationalizes a Hybrid Stewardship model where legitimacy is co-produced by code and people.
11. Ethical Fault Lines and How to Cross Them
11.1 Bias, Fairness, and Goodhart’s Trap
When a metric becomes a target, it can be gamed. If “lowest price” dominates scoring, vendors may underbid then renegotiate. A balanced multi-criteria model—lifecycle cost, reliability, and delivery history—reduces incentives for opportunism while recognizing public value beyond price.
11.2 Privacy and Surveillance Risks
Procurement data includes sensitive commercial information. AI systems must adhere to purpose limitation, access controls, and proportional retention. Privacy harms are not just legal risks; they are symbolic injuries that deplete institutional trust.
11.3 Explainability vs. Performance
High-performing models can be opaque. The way out is a tiered model stack: interpretable models for eligibility and explainable ensemble methods for scoring, with a human exception layer for edge cases. This preserves performance while meeting due-process expectations.
11.4 Security and Sovereignty
Model weights, prompts, and training data are strategic assets. States should treat them as critical infrastructure with appropriate controls, including sovereign hosting or trusted-cloud arrangements and strict supply-chain security for model components.
12. Beyond Procurement: The Temptation of Expansion
Success in procurement will invite expansion to licensing, benefits eligibility, tax compliance, and infrastructure prioritization. Each domain presents unique stakes and error asymmetries. A principled expansion requires:
Domain-specific harm assessments (who bears false positives vs. false negatives?).
Pilot-first approaches with randomized rollout to compare outcomes.
Sunset clauses to prevent lock-in if harms outweigh benefits.
13. A Thought Experiment: The AI President
Imagine an “AI president” with control over cabinet appointments, budgets, and foreign policy. Even if such a system could optimize policies against a social-welfare function, legitimacy would still falter because:
Constitutional Design: Most constitutions premise executive power on a person.
Diplomatic Practice: International law recognizes states through human representatives.
Emergency Powers: Crisis leadership requires rhetorical authority and responsibility taking—qualities that anchor obedience and sacrifice.
Therefore, an AI presidency would either be ceremonial (a dashboard with a voice) or authoritarian (a way to mask unaccountable rule behind a technocratic veneer). Neither fits a robust democracy.
14. What Albania’s Move Really Means
In sociological terms, Albania has enacted a symbolic reordering: it elevates technological capital to the ministerial field while retaining human guardians of accountability. The announcement’s performative power is substantial: it signals the end of “business as usual” in procurement and compels both bureaucrats and vendors to orient toward measurable criteria. But it does not abolish politics; it reconfigures it. The boundary work now shifts to auditors, judges, journalists, and citizens who must learn new languages—model drift, feature leakage, calibration—to keep authority answerable.
15. Conclusion: Augmented Leadership, Not Algorithmic Rule
Can AI replace presidents and ministers? Technically, parts of their tasks—yes. Sociologically and normatively, wholesale replacement is neither legitimate nor desirable. The future of statecraft lies in augmented leadership: humans responsible for value judgments, aided by machines that ensure consistency, speed, and traceability. Albania’s virtual “minister” is an early marker on that path. Its success will depend less on dazzling interfaces and more on boring but essential institutions: statutory clarity, external audits, contestation rights, and a public culture capable of debating algorithms with the same vigor once reserved for ideologies.
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