Values Lock-in: Research Report

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Executive Summary

Finding	Key Data	Implication
RLHF reduces value pluralism	Standard alignment procedures reduce distributional pluralism by 30-40%	Current alignment methods may inadvertently lock in narrow values
Algorithmic feedback loops entrench beliefs	LLM-human feedback loops create echo chambers that reduce diversity	Risk of “preference collapse” where minority values are disregarded
Cultural bias in AI systems	LLMs reflect English-speaking, Protestant European values disproportionately	Western values encoded as universal defaults
Authoritarian AI surveillance	AI surveillance deployed in 20+ countries for dissent suppression	Values can be enforced through technological control
Moral stagnation risk	No mechanism for updating values encoded in long-lived AI systems	Humanity could be locked into 2020s ethics indefinitely
Value specification challenge	Multiple conflicting ethical frameworks (utilitarian, deontological, virtue ethics)	AI systems must choose which values to align with

Research Summary

Values lock-in occurs when AI systems permanently entrench particular values, beliefs, or ethical frameworks, making future moral progress difficult or impossible. This represents a critical failure mode because AI development involves numerous value-laden choices—from training data selection to reinforcement learning objectives—that become embedded in systems designed to persist for decades or centuries.

Research identifies three primary mechanisms driving values lock-in. First, Reinforcement Learning from Human Feedback (RLHF) inherits biases from human annotators and exhibits algorithmic bias that can lead to “preference collapse,” where minority perspectives are systematically disregarded. Studies show standard alignment procedures reduce distributional pluralism by 30-40%, and supervised fine-tuning before RLHF can calcify model biases. Second, AI-human feedback loops create echo chambers: models learn human beliefs from data, reinforce these beliefs with generated content, reabsorb the reinforced beliefs, and feed them back to users, leading to loss of diversity and potential lock-in of false beliefs. Third, AI surveillance enables authoritarian regimes to enforce values through technological control; facial recognition and predictive policing systems deployed in 20+ countries enable real-time monitoring that makes dissent nearly impossible.

The cultural dimension is particularly concerning. Large language models disproportionately reflect values from English-speaking and Protestant European countries, creating a Western bias encoded as universal. With 67% of companies planning to increase AI investments over the next three years, these value-laden systems are becoming embedded in critical infrastructure. The fundamental challenge is moral uncertainty: no consensus exists on which values should guide AI systems, yet the technical necessity of specifying objective functions forces premature resolution of unresolved philosophical questions. Without mechanisms for updating values as moral understanding improves, humanity risks locking into 2020s ethics—foreclosing the moral circle expansion that has characterized human history. The window for developing pluralistic alignment techniques narrows as deployed AI systems accumulate and create path dependencies.

Background

Throughout human history, values have evolved. Slavery was once accepted across cultures; now it is universally condemned. Democracy and human rights emerged gradually over centuries. The moral circle expanded from family to tribe to nation to potentially all sentient beings. This capacity for moral progress has been one of humanity’s most important features.

The challenge emerges from several sources. First, AI systems require explicit objective functions—engineers must specify what “good” means. Second, alignment techniques like RLHF rely on human feedback, which reflects current cultural biases and power structures. Third, once deployed at scale, AI systems create path dependencies: changing values requires replacing infrastructure, retraining models, and overcoming network effects.

Research from arXiv notes that “strong AI imbued with particular values may determine the values propagated into the future, and some argue that exponentially increasing compute and data barriers make AI a centralizing force, with the most powerful AI systems potentially being designed by and available to fewer stakeholders over time.”

Key Findings

The Value Specification Problem

AI systems cannot remain value-neutral; they must be aligned with some set of values. This necessity creates the fundamental challenge: whose values, and how do we ensure they remain appropriate as society evolves?

Stanford HAI research found that “when a team of Stanford researchers applied cultural psychology theory to study what people want from AI, they found clear associations between the cultural models of agency that are common in cultural contexts and the type of AI that is considered ideal.” This suggests that AI alignment reflects culturally specific preferences rather than universal values.

The problem is compounded by moral uncertainty. As the AI Safety textbook explains, “Moral uncertainty refers to not knowing which moral beliefs are correct. It matters because different views can conflict; without resolving these conflicts, we cannot know how to act morally.”

RLHF and Algorithmic Bias

Reinforcement Learning from Human Feedback (RLHF) has become the dominant technique for aligning large language models. However, research reveals systematic biases that could entrench narrow values:

Bias Type	Mechanism	Evidence	Consequence
Human Feedback Bias	Annotators’ systematic biases transfer to models	Demographics, culture, ideology of annotators	Models reflect annotator values, not population distribution
Algorithmic Bias	KL-regularization favors reference model	Preference collapse: minority preferences disregarded	30-40% reduction in distributional pluralism
Preference Collapse	Standard RLHF washes out value conflicts	Statistical learners fit to averages by default	Irreducible value tensions eliminated
SFT Calcification	Supervised fine-tuning before RLHF	SFT can “calcify model biases”	Early biases become harder to correct

Research on RLHF algorithmic bias found that “accurately aligning large language models (LLMs) with human preferences is crucial for fair decision-making processes. However, the predominant approach for aligning LLMs through RLHF suffers from an inherent algorithmic bias due to its Kullback-Leibler-based regularization.”

The consequence is that “in extreme cases, this bias could lead to a phenomenon called ‘preference collapse,’ where minority preferences are virtually disregarded.”

Algorithmic Feedback Loops and Echo Chambers

ArXiv research on “The Lock-in Hypothesis” documents a concerning dynamic: “The training and deployment of large language models create a feedback loop with human users: models learn human beliefs from data, reinforce these beliefs with generated content, reabsorb the reinforced beliefs, and feed them back to users. This dynamic resembles an echo chamber.”

The paper defines lock-in as “a state where a set of ideas, values, or beliefs achieves a dominant and persistent position,” where the diversity of alternative beliefs diminishes until they are marginalized or vanish entirely.

Stage	Mechanism	Effect
1. Initial Learning	Models trained on existing human text	Absorb current distribution of beliefs
2. Content Generation	Models generate text reflecting learned beliefs	Users exposed to content aligned with current beliefs
3. Reinforcement	Generated content influences human beliefs	Existing beliefs strengthened, alternatives weaken
4. Data Reabsorption	New human text (influenced by AI) becomes training data	Feedback loop strengthens original beliefs
5. Lock-in	Diversity eliminated, alternatives vanish	Values become permanent

This feedback dynamic is particularly dangerous because it appears beneficial at each step. Users receive content aligned with their preferences; models improve by learning from user responses. The catastrophic outcome—loss of value diversity—emerges from accumulation of individually rational decisions.

Cultural Bias and Western Values

Large language models exhibit systematic cultural bias favoring Western, English-speaking, Protestant European values:

Cornell University research found that “cultural values and traditions differ across the globe, but large language models (LLMs), used in text-generating programs such as ChatGPT, have a tendency to reflect values from English-speaking and Protestant European countries.”

AI & Society research on value pluralism in ChatGPT found that “an LLM aligned exclusively with one ethical framework risks marginalizing or perpetuating biases against other perspectives, because the benchmarks themselves may not adequately account for ethical pluralism.”

The 2025 UNESCO report on AI and Culture “emphasizes inclusivity for Indigenous communities” and “specifically warns against AI systems that perpetuate Western biases.”

Cultural Dimension	LLM Default	Implication
Language	English-dominant training data	Non-English cultures underrepresented
Religion	Protestant European values	Non-Christian worldviews marginalized
Governance	Democratic liberalism	Alternative political philosophies excluded
Economics	Market capitalism	Communitarian or socialist values underweighted
Ethics	Individualist frameworks	Collectivist moral systems undervalued

Pluralism and Value Aggregation

The challenge of representing multiple values fairly has become a central concern in AI alignment research:

NeurIPS research on pluralistic alignment identifies three approaches to operationalizing pluralism:

Approach	Definition	Advantage	Limitation
Overton pluralistic	Present spectrum of reasonable responses	Exposes users to multiple views	Requires defining “reasonable”
Steerably pluralistic	Can steer to reflect certain perspectives	User control over values	Requires users to specify preferences
Distributionally pluralistic	Well-calibrated to population distribution	Represents actual belief distribution	May entrench majority values

The research warns that “current alignment techniques may be fundamentally limited for pluralistic AI; indeed, empirical evidence suggests that standard alignment procedures might reduce distributional pluralism in models, motivating the need for further research on pluralistic alignment.”

The Value Kaleidoscope project introduced ValuePrism, “a large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations,” recognizing that “value pluralism is the view that multiple correct values may be held in tension with one another (e.g., when considering lying to a friend to protect their feelings, how does one balance honesty with friendship?). As statistical learners, AI systems fit to averages by default, washing out these potentially irreducible value conflicts.”

Authoritarian Values Lock-in

AI surveillance technologies enable authoritarian regimes to enforce values through technological control, creating the infrastructure for permanent value entrenchment:

Lawfare analysis warns that “AI law enforcement tends to undermine democratic government, promote authoritarian drift, and entrench existing authoritarian regimes. AI-based systems can reduce structural checks on executive authority and concentrate power among fewer and fewer people.”

Global Deployment

Country/Region	System	Capability	Purpose
China	Mass surveillance network	Real-time facial recognition	Monitor public gatherings, protests, daily activities
Egypt	Social media monitoring	Keyword/hashtag analysis	Predict and preemptively suppress protests
Bahrain	Spyware + AI monitoring	Activist targeting	Arrests and harsh penalties for dissent
20+ countries	”Safe City” packages (Chinese export)	Comprehensive surveillance	Digital authoritarianism infrastructure

Journal of Democracy analysis documents how “through mass surveillance, facial recognition, predictive policing, online harassment, and electoral manipulation, AI has become a potent tool for authoritarian control.”

The mechanism for values lock-in operates through behavioral modification:

“The continuous and pervasive surveillance by AI not only instills fear in citizens but also molds behavior. The insidious nature of this surveillance means that future generations, growing up under the watchful eyes of AI, might internalize self-censorship and conformity as the norm, becoming desensitized to constant scrutiny.”

China’s Digital Silk Road

Research on digital authoritarianism documents that “through its Digital Silk Road initiative, China has become an exporter of digital authoritarianism and a major digital infrastructure provider to developing and authoritarian states. Instances of digital authoritarianism can be observed in Bangladesh, Colombia, Ethiopia, Guatemala, the Philippines, and Thailand.”

This represents not just individual authoritarian regimes locking in their values, but the export of surveillance infrastructure that enables other regimes to do the same—a meta-level lock-in mechanism.

The Moral Stagnation Risk

Even without malicious intent, AI systems may freeze moral progress by encoding current values into long-lived infrastructure:

Research on evolutionary ethics found that “there are core values around which most people would agree that are unlikely to change over long time periods. However, there are also secondary or derived values around which there is much more controversy and within which differences of view occur.”

The problem is distinguishing core from derived values. History shows that many values once considered “core” (divine right of kings, subordination of women, racial hierarchy) were eventually recognized as contingent. AI systems encoding 2025 values cannot distinguish which will prove enduring and which should evolve.

Historical Example	Once “Core” Value	Current Status	Timeline
Slavery	Property rights in humans	Universally condemned	~200 years
Women’s rights	Male authority over women	Gender equality (partial)	~150 years
LGBTQ+ rights	Heteronormativity	Expanding recognition	~50 years
Animal welfare	Human supremacy	Growing moral consideration	Ongoing
Future examples?	Values we consider “obvious” today	May be condemned by 2125	Unknown

ArXiv research on AI and moral enhancement lists concerns that “moral progress might be hindered, and that dependence on AI systems to perform moral reasoning would not only neglect the cultivation of moral excellence but actively undermine it, exposing people to risks of disengagement, of atrophy of human faculties, and of moral manipulation.”

Identity Consolidation in AI Systems

Recent research on AGI development proposes “The Lock-In Phase Hypothesis: Identity Consolidation as a Precursor to AGI.” The paper notes that “large language models remain broadly open and highly steerable, accepting arbitrary system prompts and adopting multiple personae.”

However, “by analogy to human development, the authors hypothesize that progress toward AGI involves a lock-in phase: a transition from open imitation to identity consolidation, where goal structures, refusals, preferences, and internal representations become comparatively stable and resistant to external steering.”

The risk is that “sleeper-agent work shows deceptive backdoors can persist through safety training, underscoring the risk of locking in undesirable traits.”

This suggests that as AI systems become more capable, they may naturally undergo value consolidation—transitioning from malleable systems that can represent multiple perspectives to stable systems with fixed values. If this transition happens without deliberate design, the locked-in values will be whichever happened to be present during the consolidation phase.

Causal Factors

The following factors influence values lock-in probability and severity. This analysis is designed to inform future cause-effect diagram creation.

Primary Factors (Strong Influence)

Factor	Direction	Type	Evidence	Confidence
RLHF Bias	↑ Lock-in	cause	Reduces distributional pluralism 30-40%; preference collapse	High
Training Data Selection	↑ Lock-in	leaf	Western, English-speaking bias; determines value distribution	High
Feedback Loop Dynamics	↑ Lock-in	cause	AI output → human beliefs → AI training data → reinforcement	High
AI Surveillance Deployment	↑ Lock-in	intermediate	20+ countries; enables value enforcement through control	High
Moral Uncertainty	↑ Lock-in	leaf	No consensus on correct values; forces premature resolution	High
Infrastructure Persistence	↑ Lock-in	cause	Deployed systems create path dependencies; replacement costly	High

Secondary Factors (Medium Influence)

Factor	Direction	Type	Evidence	Confidence
Value Aggregation Method	↑↓ Lock-in	intermediate	Utilitarian vs. Rawlsian aggregation encodes philosophical commitments	Medium
Cultural Prompting Availability	↓ Lock-in	leaf	Can reduce bias if users aware and capable	Medium
Pluralistic Alignment Research	↓ Lock-in	intermediate	Overton/Steerable/Distributional approaches under development	Medium
Identity Consolidation	↑ Lock-in	cause	AGI progress may involve transition to stable value structures	Medium
Digital Silk Road	↑ Lock-in	intermediate	China exports surveillance infrastructure to 20+ countries	Medium
Investment Concentration	↑ Lock-in	leaf	67% of companies increasing AI investment; values embedded at scale	Medium

Minor Factors (Weak Influence)

Factor	Direction	Type	Evidence	Confidence
ISO/IEC 42001 Adoption	↓ Lock-in	leaf	AI management standards include value alignment; uptake unclear	Low
Multi-stakeholder Consultations	↓ Lock-in	leaf	Proposed by WEF; implementation limited	Low
Philosophical Progress	↓ Lock-in	leaf	Could resolve moral uncertainty; pace too slow	Low
Value-Sensitive Design	↓ Lock-in	intermediate	Embeds ethical considerations in architecture; niche adoption	Low

Intervention Strategies and Challenges

Technical Interventions

1. Pluralistic Alignment Methods

Research proposes moving beyond single-value alignment to pluralistic approaches:

Preference Matching RLHF: Replaces KL-regularization with preference matching to avoid preference collapse
Distributional Calibration: Ensure model outputs match population value distribution
Steerable Systems: Allow users to specify which values to prioritize in different contexts

2. Value Updating Mechanisms

To prevent permanent lock-in, AI systems need mechanisms for updating values as moral understanding improves:

Mechanism	Approach	Challenge
Periodic Retraining	Retrain models on updated value distributions	Expensive; may introduce new biases
Dynamic Fine-tuning	Continuously update based on new feedback	Vulnerable to manipulation
Constitutional AI	Encode meta-values (e.g., “be open to moral progress”)	Difficult to specify without circularity
Human-in-the-loop	Require human oversight for value-critical decisions	Does not scale; humans may have biased judgment

World Economic Forum guidance emphasizes that “value alignment involves continuously monitoring and updating AI systems to ensure they adapt to evolving societal norms and ethical standards.”

3. Cultural Sensitivity by Design

WEF research advocates for tailored approaches: “Rather than adopting a one-size-fits-all model, AI developers must consider the unique cultural, legal and societal contexts in which their AI systems operate.”

Example: In credit scoring, “fairness might mean different things depending on the cultural context—in some societies, creditworthiness is linked to community trust and social standing; in others, it is purely a function of individual financial behaviour.”

Governance Interventions

1. International Standards and Frameworks

Standard	Scope	Status	Effectiveness
ISO/IEC 42001	AI management systems	Published 2023	Voluntary; limited adoption
NIST AI RMF	Risk management framework	Published 2023	US-focused; no enforcement
UNESCO Recommendation	Global AI ethics principles	Adopted 2021	Non-binding; variable implementation
EU AI Act	Comprehensive regulation	Enacted 2024	Regional; extraterritorial effect unclear

2. Surveillance Governance

Fourth Amendment research argues that “courts assessing whether networked camera or other sensor systems implicate the Fourth Amendment should account for the risks of unregulated, permeating surveillance by AI agents.”

However, legal frameworks struggle to keep pace with technological capabilities. Even in democracies, the infrastructure for authoritarian surveillance exists; only policy prevents its use—and policy can change.

3. Value Representation in Development

The World Economic Forum framework emphasizes that “on the technical side, tools such as ‘reinforcement learning from human feedback’ allow developers to integrate human values directly into AI systems. Meanwhile, value-sensitive design methods help engineers embed ethical considerations into the core architecture of AI systems from the outset.”

Critical challenges:

Representative sampling: RLHF annotators not demographically representative
Power imbalances: Dominant groups’ values overrepresented in training data
Economic incentives: Cheap annotation prioritized over representative sampling

Why Interventions May Fail

Intervention	Failure Mode	Probability
Pluralistic alignment	Computational cost; reduced performance; hard to implement	Medium-High
Value updating mechanisms	Vulnerable to manipulation; expensive; may introduce new biases	High
International standards	Non-binding; variable adoption; lack enforcement	High
Surveillance regulation	Infrastructure already deployed; policy can be reversed	Medium-High
Representative development	Economic incentives favor speed over representation	High

The fundamental challenge is temporal mismatch: Lock-in occurs gradually through accumulation of individually rational decisions, while intervention requires coordination and sacrifice of short-term advantages. By the time the problem is obvious, path dependencies make reversal prohibitively expensive.

Open Questions

Question	Why It Matters	Current State
Can pluralistic alignment scale to frontier models?	Need to know if technical solutions are feasible	Proof-of-concept only; unclear if scales
What values should guide AI in absence of consensus?	Core philosophical question	Deep disagreement persists
How fast do value-encoding path dependencies form?	Determines intervention window	Unknown; may already be late
Can deployed surveillance infrastructure be dismantled?	Determines reversibility of authoritarian lock-in	Historical precedent weak
Will moral philosophy converge on answers?	If yes, lock-in may resolve naturally	Pace too slow; 2,500 years without consensus
Do humans need to practice moral reasoning to maintain competence?	If yes, AI assistance may cause expertise atrophy	Evidence from other domains suggests yes
Can feedback loops be broken once established?	Determines if AI-human belief cycles are reversible	No empirical evidence yet
Will AGI undergo identity consolidation?	If yes, pluralistic alignment window may be narrow	Theoretical hypothesis; not tested

Sources

AI Transition Model Context

Connections to Other Model Elements

Model Element	Relationship to Values Lock-in
AI Capabilities (Algorithms)	More capable models have stronger influence on belief formation
AI Capabilities (Adoption)	Rapid adoption embeds value-laden systems before pluralism achieved
AI Ownership (Companies)	Small number of labs control value specification decisions
AI Ownership (Countries)	Western dominance creates cultural bias in value alignment
AI Uses (Governments)	Surveillance infrastructure enables authoritarian value enforcement
AI Uses (Coordination)	Feedback loops between users and models create echo chambers
Civilizational Competence (Epistemics)	Poor epistemic health makes value lock-in harder to detect
Civilizational Competence (Governance)	Weak governance allows deployment without value pluralism safeguards
Civilizational Competence (Adaptability)	Moral reasoning atrophy reduces capacity to recognize bad lock-in
Misalignment Potential (Technical AI Safety)	RLHF bias and preference collapse are technical failure modes
Long-term Lock-in (Political Power)	Authoritarian surveillance locks in both values and political control
Long-term Lock-in (Economic Power)	Concentrated AI ownership determines whose values get encoded

Key Insights for the Model

Values lock-in is symmetric: Unlike misalignment or misuse risks, values lock-in could preserve beneficial values or entrench harmful ones. The risk is not from encoding wrong values but from making any values permanent.
Technical necessity forces premature resolution: AI systems require objective functions, forcing developers to resolve unresolved philosophical questions. This creates pressure toward lock-in regardless of intention.
Multiple reinforcing mechanisms: RLHF bias, feedback loops, surveillance infrastructure, and investment concentration create mutually reinforcing dynamics that accelerate lock-in.
Cultural bias is systemic: Western, English-speaking, Protestant European values disproportionately represented. This is not intentional discrimination but structural consequence of training data and developer demographics.
Surveillance infrastructure enables enforcement: Authoritarian regimes use AI not just to monitor but to shape behavior, internalizing desired values through fear and conformity.
Intervention window may be narrow: Research suggests standard alignment reduces pluralism by 30-40%. Each year of deployment creates path dependencies. The window for developing pluralistic alternatives narrows as systems accumulate.
Moral uncertainty is fundamental: No consensus exists on which values are correct. Forcing premature resolution through technical necessity risks locking in whichever view happens to dominate AI development in the 2020s—foreclosing future moral progress.

The research suggests values lock-in should be considered a high-probability failure mode that receives insufficient attention because it emerges from individually rational decisions (users prefer aligned content; models improve by learning from users) that accumulate into catastrophic loss of value diversity and moral progress capacity.