AI Models as Microgovernments: Understanding AI’s Role in Society
AI model operates as a microgovernment, with its own governance structures, laws, policies, and constituencies. Time to dig a little deeper.
AI models have transcended their roles as mere tools to become autonomous agents shaping the world, for better or for worse. From generating code to recommending products, AI models increasingly influence human behaviour, decision-making, and productivity. These generated outputs steer the direction of entire populations: developers, end-users, enterprises, and even interconnected AI systems.
When viewed this way, each AI model operates as a microgovernment, with its own governance structures, laws (model parameters), policies (the training data), and constituencies (users and applications whether this be human driven or machine driven).
This perspective could potentially reframes our understanding of AI’s societal role and invites us to examine AI models not just as technological systems but as governing entities that wield power over digital populations.
AI Models as Microgovernments: A Conceptual Framework
1. Laws and Governance: The Role of Parameters and Rules
Every AI model is governed by its parameters — the foundational laws that dictate its behaviour. In a microgovernment we see this:
- Parameters = Laws: These are immutable within a trained model, defining the outcomes users can expect within the model’s jurisdiction.
- Hyperparameters = Constitutional Design: Decisions around model architecture (e.g., GPT vs. LLaMA) shape how an AI government “thinks” and responds.
- Fine-Tuning = Policy Amendments: External adjustments to align the model’s behaviour with specific applications, much like new policies responding to societal needs.
The parameters of an AI model are its core building blocks, much like the laws of physics or economics underpinning real-world governance. These laws are encoded during training, where billions of weights and biases are fine-tuned to achieve an objective. However, even small adjustments to these parameters — akin to minor changes in a legal code — can produce disproportionate follow-on effects. This phenomenon mirrors the Butterfly Effect, where tiny changes in an initial state lead to significant and often unpredictable outcomes over time.
Edward Lorenz’s version of the butterfly effect: For a sufficiently complex system, a small change in the present quickly balloons into a large change in the future. Lorenz’s example was a butterfly flapping its wings in Brazil subsequently producing a tornado in Texas. Los Alamos research demonstrated that quantum systems can do the same, such that a slight change in initial conditions can cause a quantum wave function to diverge wildly with time. Mathematically we can cause similar effects within AI model weights.
Microtonal Changes and the Butterfly Effect
When an AI model processes data, it relies on an intricate network of parameters working in concert. The introduction of a tiny shift — such as modifying a single weight during fine-tuning — can propagate across the network and influence outputs in unexpected ways:
- Semantic Shifts: A slight adjustment to how a language model processes certain words can subtly change the tone, sentiment, or accuracy of responses. For example, tweaking weights related to words like “risk” or “safety” might have wide-reaching consequences for outputs in financial or medical domains.
- Behavioural Biases: Small changes can amplify latent biases in the training data, altering outputs disproportionately for specific groups or contexts.
- Performance Trade-offs: Improving the accuracy of a single task (e.g., generating creative text) might compromise another function (e.g., factual consistency), much like policy changes that favour one constituency at the expense of another.
The interconnected nature of an AI model’s parameters ensures that no change exists in isolation. These microtonal adjustments ripple through the model, creating a delicate balance between stability and adaptability. Much like governance systems, AI models must manage these cascading effects carefully to avoid unintended consequences.
Implications for Stability and Governance
The sensitivity of AI parameters underscores the importance of rigorous testing, monitoring, and accountability:
Stability
Just as governments avoid overly frequent or disruptive legal changes, AI developers must ensure that fine-tuning and updates do not destabilise the model’s performance. How to test these outcomes is a huge challenge.
Governance by Design
Small, intentional tweaks and changes must be governed by frameworks that anticipate downstream effects, akin to regulatory impact assessments in policymaking.
Transparency
Users and developers must understand how parameter changes affect outputs, fostering trust in the AI microgovernment’s decision-making process. This would suggest a completely open changelog of the model changes, open sourcing the testing framework and results.
For example, an AI model used in automated content moderation might unintentionally suppress critical but non-violative discourse due to minor changes in its sensitivity thresholds. Such unintended “policy” effects highlight the importance of balancing stability with adaptability in AI governance.
Implication: Much like micro-states, AI models have limitations in scope and jurisdiction — what they can govern (e.g., language, vision, decision-making). The sensitivity of parameters necessitates careful stewardship, and therefore a huge amount of trust and accountability, ensuring that minor adjustments do not lead to disproportionate or destabilising outcomes.
2. Training Data: The Formation of Policies and Biases
Training data serves as the bedrock of any AI model’s governance. It represents historical decisions, policies, and cultural norms that define the model’s outputs:
Training Data = Historical Precedent
AI models reflect the norms embedded in their training corpus. Just as societies inherit cultural frameworks from history, AI models inherit biases and values from their data. As we know from general AI model training, the better quality of the data, the better quality of the output.
Bias = Policy Blind Spots
Models inherit biases much like governments may create laws that unintentionally favour specific constituencies. Bias will always appear in any model, it’s how it’s handled by the owners of the model.
Model Updates = Policy Revisions
Periodic retraining and model updates resemble iterative policymaking to address societal evolution. How revisions are logged and debated before being available to the public (users and applications) needs thoroughly documenting.
Training Data as Historical Precedent
Training data is analogous to historical precedent that defines what is acceptable or likely within the AI’s jurisdiction. Just as legal systems reference case law to make judgments, an AI model references patterns in its training data to determine outputs. For example:
- A language model trained on historical texts may reflect outdated norms or linguistic biases, perpetuating systemic prejudices unless explicitly corrected.
- A recommendation algorithm trained on user behaviour might amplify previously popular content, creating a “feedback loop” that reinforces dominant trends while marginalising alternative voices.
The weight of this historical precedent is immense because the training data determines what the AI model knows and prioritises. Like laws shaped by historical societies, training data reflects the assumptions and biases of the time and context in which it was gathered.
Bias as Policy Blind Spots
Bias in training data operates as a policy blind spot: unintended gaps or imbalances that impact certain constituencies unfairly. These biases may arise from:
Sampling Issues:
Insufficient representation of diverse groups or contexts, leading to skewed outputs.
Cultural Biases:
Overrepresentation of dominant narratives that marginalise minority perspectives.
Algorithmic Bias Amplification:
Reinforcement of biases when models rely on incomplete or unbalanced feedback loops.
For instance, facial recognition systems trained predominantly on lighter-skinned datasets have historically underperformed for darker-skinned individuals. This bias mirrors inequities in societal laws that fail to serve all citizens equally.
Model Updates as Policy Revisions
Just as governments revise laws to address emerging societal challenges, AI models undergo updates to rectify biases, incorporate new knowledge, and improve outputs. However, these revisions must balance continuity with adaptation:
- Retraining Models: Introducing new data to correct blind spots or reflect evolving norms.
- Fine-Tuning for Specific Use Cases: Aligning outputs with contextual requirements without destabilising performance elsewhere.
- Monitoring for Drift: Ensuring that models do not regress or reintroduce past biases after updates.
For example, periodic updates to a content moderation model might adapt to emerging slang or cultural norms, much like legal revisions that account for societal evolution. Social media trends change and update at a rapid rate. Incorporating these societal changes may be difficult to implement in a timely fashion.
Implications for AI Governance
Integrity of Training Data: Just as historical records must be scrutinised, training data must be curated to minimise bias and ensure diverse representation.
Bias Mitigation Strategies: AI developers must proactively identify and correct policy blind spots through transparency, auditing, and ongoing feedback.
Adaptive Policy Updates: Model revisions must balance stability with responsiveness, ensuring continuity while addressing emerging challenges.
The “governance quality” of an AI model depends on the integrity, diversity, and transparency of its training policies (data). Like governments, opaque policies can create mistrust, while carefully curated updates ensure fairness and adaptability.
3. Constituencies: The AI User Population
Just as governments serve citizens, AI models serve their constituencies:
Direct Users = Citizens: Individuals interacting with the model (e.g., users of ChatGPT or Stable Diffusion).
API Calls = Economic Activity: Developers leveraging AI outputs for their applications are analogous to economic actors operating under a government.
Costing Policies = Economic Regulation: Changes to pricing or resource costs for model queries can drastically affect economic activity and decision quality.
Costing Policies and Decision Quality
In AI microgovernments, the “cost of queries” serves as a form of economic regulation that can significantly impact constituents. Developers and businesses operate within the jurisdiction of the AI model, relying on its outputs to inform decisions and create value. However, changes in pricing — whether to API costs or model access — can create systemic second- and third-order consequences:
Resource Scarcity
If API costs increase, businesses may limit their queries, reducing their ability to generate high-quality outputs. For example, startups relying on an AI model for product recommendations may reduce API calls, leading to less accurate personalisation and lower customer satisfaction.
Decision Trade-offs:
When access to AI becomes expensive, organisations prioritise certain tasks over others. This trade-off can degrade overall decision quality, as cost constraints force businesses to sacrifice nuance, precision, or experimentation.
Market Inequities
Smaller organisations with limited budgets may be priced out of using cutting-edge AI, exacerbating disparities between businesses of different sizes — akin to economic policies that disproportionately affect low-income populations.
Second- and Third-Order Consequences
Changes to pricing policies do not occur in isolation. Much like tax increases in a national economy, adjustments to the cost of queries ripple through ecosystems:
- Innovation Stagnation: Higher costs discourage experimentation, reducing the diversity and novelty of solutions developed using AI. The availability in GPU compute power has a direct impact on model generation.
- Systemic Migrations: Developers may shift to alternative AI models, creating fragmentation across platforms and reducing overall stability.
- Long-Term Trust Erosion: Unpredictable or opaque changes to pricing policies undermine confidence in the AI microgovernment’s economic regulation, leading to distrust and reduced adoption.
AI microgovernments must carefully evaluate the cost implications of their policies, ensuring they balance economic sustainability with equitable access and high-quality decision-making.
Feedback Loops as Democratic Participation
Systems like reinforcement learning from human feedback (RLHF) mirror participatory governance, where user behavior and input shape model policies. However, participation must remain accessible and meaningful to all constituencies to avoid systemic inequities.
AI models must serve as equitable economic regulators, minimising unintended consequences of pricing changes while fostering a stable and innovative ecosystem.
4. Outputs: The Policy Decisions of AI Governments
Every AI model produces outputs that influence individual and collective behaviors:
Outputs = Policy Outcomes: Model-generated outputs (e.g., code suggestions, ad recommendations, financial scoring, employment filters, and benefit decisions) act as directives that nudge human behaviour.
Utility and Fairness = Governance Quality: The effectiveness of outputs determines the legitimacy of the AI model’s role as a microgovernment.
Beyond Healthcare: Outputs in Societal Systems
While AI’s role in healthcare diagnostics highlights high-stakes decisions, the influence of AI models extends far beyond. Consider:
Benefit Eligibility Decisions: AI models deployed to evaluate welfare or unemployment benefits can impact entire populations by determining eligibility and payouts. Errors or biases in these systems can exclude vulnerable groups, much like flawed public policies.
Employment Screening: Hiring platforms rely on AI to filter candidates, decide on interview selections, and even assess skill fit. Biased or flawed outputs can exclude deserving applicants, leading to systemic inequities in labor markets.
Financial Credit Scoring: AI-based credit systems determine loan approvals, mortgage access, and insurance rates. Outputs that favour certain demographics or reinforce historical biases can widen economic disparities.
Union Negotiations and Labor Automation: AI models assessing productivity or optimising labor costs can influence union negotiations, workforce downsizing, or the automation of roles — reshaping employment landscapes.
Judicial Systems: In legal contexts, AI tools assist with sentencing recommendations, bail assessments, or evidence analysis. Outputs that perpetuate biases in sentencing history undermine fairness in judicial processes.
Each of these examples reflects AI outputs as high-impact policy decisions with far-reaching consequences for individuals and societies. Small errors, biases, or cost-driven optimisations can escalate into systemic failures, much like flawed policies in human governance. The effects of these failures are felt for years within the population.
Second-Order and Third-Order Consequences
Trust and Legitimacy: Flawed outputs in benefit systems or employment filters erode trust in both AI systems and the institutions deploying them.
Systemic Exclusion: AI errors can disproportionately impact marginalised communities, leading to long-term exclusion from opportunities and economic stability.
Behavioural Nudging: Outputs like recommendation algorithms subtly nudge behaviours, influencing public opinion, consumption patterns, and societal priorities.
Outputs must be continuously monitored, tested, and aligned with ethical standards. Much like governments are judged by their policy outcomes, AI models must ensure fairness, transparency, and accountability in their outputs.
The Dynamics of AI Microgovernment Legitimacy
For any government to be legitimate, it must balance three key dimensions:
Performance:
Does the AI model fulfil its intended purpose effectively? Users expect reliable, consistent and high-quality outputs. Performance legitimacy is tied to the accuracy and reliability of outputs. For instance, a language model used for translation must consistently deliver precise and culturally appropriate translations to maintain trust. Failures in performance — such as high error rates in critical applications like legal document drafting — undermine the perceived competence of the AI government.
Transparency:
Are the policies (training data, fine-tuning rules) clear and accessible? Trust erodes when governance is opaque. Transparency does not require full exposure of proprietary details but does demand that users understand the boundaries and biases of a model. Clear documentation and explainability features enhance trust, especially in applications like financial credit scoring or judicial sentencing where opaque decisions can spark public outrage.
Fairness:
Does the AI serve all constituencies equitably, avoiding bias or undue harm? Fairness extends to ensuring that marginalised groups are not disproportionately harmed by model outputs. For example, recruitment algorithms must be scrutinised for biases that exclude certain demographics from employment opportunities. Fairness also involves maintaining equitable access, ensuring that AI services do not become prohibitively expensive for smaller or underserved populations.
Additional Dimensions to Consider
- Adaptability: Can the AI model evolve with societal changes? Governance systems must remain dynamic to address emerging needs. For instance, models deployed for fraud detection in financial services must adapt as fraudulent techniques evolve, ensuring relevance and continued legitimacy.
- Ethical Alignment: Are the model’s outputs aligned with societal values? Models that violate ethical norms — for example, generating deepfakes for malicious purposes — risk delegitimisation and stricter external regulations.
Restoring Legitimacy Post-Failure
Much like human governments, AI models can recover from failures if corrective measures are implemented swiftly and transparently. Strategies include:
- Root Cause Analysis: Identifying the factors behind failures or biases in outputs.
- Community Engagement: Incorporating user feedback to refine and revalidate model outputs.
- Public Accountability: Transparent communication about corrective actions taken, building trust and restoring confidence in the model’s governance.
If an AI model fails on any of these dimensions, users (citizens) will migrate to alternative models, much as people seek better governance in the real world. Addressing these facets ensures that AI microgovernments not only maintain but also enhance their legitimacy over time.
The Future: Governing AI Microgovernments
Recognizing AI models as microgovernments forces us to address the following challenges:
AI Oversight and Regulation: Should international accords establish guidelines for cross-border AI governance? As AI increasingly influences global commerce, law, and societal behaviours, oversight mechanisms must address the challenges of harmonising regulations across nations. A potential model could include an “AI Charter,” akin to the UN’s Universal Declaration of Human Rights, outlining shared ethical principles and technical standards for AI systems.
User Representation: How can participatory mechanisms like reinforcement learning from human feedback (RLHF) be enhanced to ensure equitable user representation? Current feedback systems tend to favor majority opinions, potentially marginalising minority voices. Future governance structures could integrate proportional representation methods, enabling all constituencies to influence policy decisions.
Accountability
If an AI model makes harmful decisions, who bears responsibility? This challenge necessitates clear accountability frameworks where creators, deployers, and end-users share differentiated but interconnected responsibilities. For instance, companies deploying AI in critical industries like healthcare should be required to conduct impact assessments, similar to environmental regulations.
Transparency-by-Design
AI systems must embed transparency at every stage of their lifecycle — from training to deployment. Explainable AI (XAI) techniques can make decisions interpretable without compromising intellectual property or security. Governments and corporations alike must invest in tools that make AI decisions comprehensible to non-technical audiences.
Dynamic Adaptation
Governance frameworks must be agile enough to address emerging challenges. For example, as quantum computing and other advanced technologies intersect with AI, regulatory systems must anticipate potential disruptions and recalibrate policies proactively.
Ethical Guardrails
Establishing strong ethical guardrails is essential to prevent AI misuse. This includes safeguarding against biased training data, discriminatory outputs, and harmful applications like surveillance-based social credit systems.
A Roadmap for AI Microgovernment Governance
Global AI Alliances: Foster international coalitions that share AI research, align ethical standards, and enforce compliance with agreed-upon norms.
Participatory AI Platforms: Develop platforms where diverse user groups can provide ongoing feedback, shaping model updates and policies in real time.
AI Risk Audits: Mandate periodic risk audits for high-impact AI models, ensuring they meet ethical, legal, and performance benchmarks.
Digital Ombudsman: Create independent bodies to investigate and mediate disputes arising from AI decisions, ensuring accountability without stifling innovation.
The governance of AI microgovernments represents a profound societal challenge and opportunity. By proactively addressing these issues, we can shape a future where AI systems operate not just as efficient tools, but as equitable and accountable digital institutions.
The microgovernment framework thinking provides a lens as a starting point for understanding the role of AI in society.
Each AI model operates as a governing entity, shaping behaviours, guiding decisions, and influencing economic activity within its digital jurisdiction. This perspective demands we rethink how AI models are designed, governed, and held accountable to align with societal values.
To achieve this, the future must focus on fostering collaborative frameworks such as global AI alliances, participatory AI platforms, and dynamic governance systems that adapt to emerging technologies and challenges.
Transparency, ethical alignment, and accountability must underpin every aspect of AI development and deployment. By incorporating diverse user feedback, enforcing robust risk audits, and embedding ethical safeguards, we can ensure that AI models serve all constituencies equitably and effectively.
The responsibility lies not just with developers but with policymakers, stakeholders, and society as a whole to shape AI systems into equitable and accountable digital institutions. By doing so, we can harness the transformative potential of AI to drive innovation, enhance productivity, and create a fairer, more inclusive digital future.