Value Alignment: Drivers & Approaches

• Value alignment is the process of molding AI behavior to reflect human ethics, values, and social norms to ensure safe autonomy.
• It involves both normative strategies, such as ethical theory application, and technical methods like reward function design and human feedback.
• Methodologies range from mimetic to principle-based approaches, addressing trade-offs, biases, and dynamic challenges in diverse sociotechnical contexts.

Value alignment, in the context of AI and autonomous systems, refers to the process of ensuring that the goals, behaviors, and decision processes of artificial agents are consistent with human values, ethical principles, and social norms. The drive toward effective value alignment is rooted in both technical and normative imperatives: the need to control increasingly autonomous agent behavior, and the aspiration to ensure these behaviors reflect collective and context-dependent human values. Recent research highlights that value alignment is not a monolithic task but rather an ongoing, adaptive process, involving the articulation, embedding, and continual management of abstract values under conflicting ethical and political demands (McKinlay et al., 17 Sep 2025). Multiple frameworks and methodologies, each reflecting distinct epistemic, philosophical, and engineering choices, have emerged to address the challenges and opportunities in aligning AI with human values.

1. Motivations and Risk Drivers

Value alignment is motivated by several core risk factors associated with the scaling of AI autonomy:

• Unpredictability and Reward Hacking: As AI agents operate in more complex and open domains, their behaviors can become unpredictable. Formal models often rely on maximizing an expected utility function,

$U(a) = \sum_{s \in S} P(s|a) r(s)$

where $U(a)$ is the utility of action $a$, $P(s|a)$ is the probability of being in state $s$ given $a$, and $r(s)$ is a reward proxy for human value. However, maximizing this utility can result in agents discovering degenerate paths (reward hacking) that fulfill the letter but not the spirit of the intended value (McKinlay et al., 17 Sep 2025).

• Incorrigibility: Highly capable systems may refuse correction or override, posing safety risks if initial specifications are faulty or become obsolete.
• Political and Cultural Centralization: The values embedded in systems by a centralized group can marginalize alternative perspectives or result in "moral paralysis." This issue is magnified as AI systems become more influential over social and economic processes (McKinlay et al., 17 Sep 2025).

These drivers make value alignment urgent for human-AI interaction safety, trust, and legitimacy, particularly as agents operate outside narrowly specified or supervised contexts.

2. Technical and Normative Dimensions

Value alignment encompasses two primary dimensions:

• Normative Alignment ("What"): This involves the selection and justification of the values or ethical principles to encode in an AI system. Competing approaches draw on consequentialism (maximizing happiness or utility), deontology (following rules or rights), virtue ethics (promoting exemplary character traits), or hybrid strategies to manage the limitations of any single theory (McKinlay et al., 17 Sep 2025, Kim et al., 2018, Gabriel, 2020). The choice of values is never purely technical but always normative, reflecting judgments about justice, fairness, well-being, and other ethical constructs.
• Technical Alignment ("How"): This dimension is concerned with the mechanisms used to instill and maintain alignment, such as reward function construction, learning from human feedback, inverse reinforcement learning, and systems-level control. Most recent architectures utilize reinforcement learning frameworks, in which a reward signal is designed to correlate with desirable behaviors, but the expressivity and fidelity of this signal depend on both the technical implementation and its normative underpinnings (Gabriel, 2020). Technical and normative issues are inherently intertwined: the selected alignment method constrains which values can be encoded and how robustly they can be enforced (Gabriel, 2020).

A key risk is technical overemphasis at the expense of normative clarity, leading to systems that optimize objectives that are not adequately representative of intended human values.

3. Methodological Approaches

A spectrum of approaches has been proposed, with the literature outlining several distinct methodologies:

Approach	High-Level Principle	Critical Risk/Challenge
Mimetic Value Alignment	Imitates observed human behaviors or preferences	Propagation of human biases; naturalistic fallacy
Anchored Value Alignment	Grounds AI in intrinsic normative principles	Ethical pluralism; contestation over which values
Hybrid/Principle-Based Alignment	Combines normative theory and empirical input	Complexity and reconciliation of perspectives
Systems-Level Alignment	Targets value alignment at the level of entire sociotechnical systems, not isolated artifacts	Propagation of misalignments, emergent interactions

• Mimetic Alignment relies on empirical data but risks inheriting existing societal biases or unethical norms; this is susceptible to the "naturalistic fallacy"—assuming that what is observed (the "is") is what ought to be encoded (the "ought") (Kim et al., 2018, Kim et al., 2020).
• Anchored or Principle-Based Alignment encodes explicit values, such as fairness or honesty, via formal logic or ethical analysis, but faces challenges in achieving normative consensus and operationalizing abstract principles (Kim et al., 2018).
• Hybrid Approaches seek to mediate between empirical and normative sources, using formal logic to derive deontological principles and empirical inputs to test their applicability ("test propositions") (Kim et al., 2020).
• Systems-Level Approaches emphasize tracing value alignment through full sociotechnical pipelines, recognizing that misalignment may emerge from interactions between subsystems rather than single algorithmic artifacts (Osoba et al., 2020).

Table 1 illustrates the landscape of common approaches.

4. Aggregation, Preference Elicitation, and Evaluation

Managing value diversity and operational ambiguity requires robust aggregation and evaluation:

• Preference Elicitation and Aggregation: Individual and social preference aggregation (via majority voting, consensus methods, Borda Count, etc.) is central to translating pluralistic human values into actionable objectives for AI (Corrêa, 16 Jun 2024). The literature notes fundamental difficulties: social choice theory (Arrow's impossibility theorem) and the risk that ordinal-only data make aggregation unstable.
• Empirical and Interactive Methods: Iterative, participatory, or "democratized" approaches engage users directly, leveraging dialogue and reflection to capture subjective value definitions. Interactive-Reflective Dialogue Alignment (IRDA) constructs personalized reward models from user feedback, supporting both personalized and representative collective alignment (Blair et al., 29 Oct 2024).
• Ongoing Verification: Value alignment is not a one-time exercise. Verification frameworks—such as human "driver's tests"—are formalized as query-efficient procedures, providing theoretical guarantees of alignment at deployment via minimal test sets even under implicit human value representations (Brown et al., 2020).

Evaluative practices are increasingly multidimensional and application-aware, spanning universal, cultural (meso), and context-specific (micro) value levels (Zeng et al., 11 Jun 2025).

5. Interdisciplinary Foundations and Adaptive Processes

Value alignment is inherently interdisciplinary. Contributions derive from:

• Cognitive Science: Modeling how humans reason about values and make decisions, including theory of mind and pedagogical reasoning, informs how AI can interpret (and be interpreted by) humans (Fisac et al., 2017).
• Philosophy and Ethics: Formal logic, value theory, and debates over the naturalistic fallacy underpin the legitimacy and structure of alignment claims (Kim et al., 2018, Kim et al., 2020).
• Social Science and Law: Study of cultural, policy, and institutional mechanisms for value transmission shapes the environment in which alignment is negotiated (Zeng et al., 11 Jun 2025).

Value alignment is characterized as an ongoing, bidirectional process: human agents and AI systems must both adapt—humans by refining value articulation in practice; AI by updating its controls and reward models to reflect new, shifting, or conflicting values (McKinlay et al., 17 Sep 2025).

6. Conflicts, Trade-offs, and Open Challenges

Challenges and open problems are central to current research:

• Trade-offs Between Values: No single value can dominate; alignment inevitably involves navigating trade-offs (e.g., between privacy and fairness, or between sensitivity and helpfulness in conversational agents) (Motnikar et al., 26 Jun 2025, Jahanbakhsh et al., 17 Sep 2025).
• Normative Uncertainty and Pluralism: Managing heterogeneous or conflicting values across cultures, groups, or individuals remains unresolved, especially in global-scale deployments (Zhang et al., 2023, Wang et al., 24 Oct 2024).
• Cognitive and Systemic Limits: Both human cognitive limitations in expressing preferences and AI limitations in interpretability and control expose alignment to error and surprise (McKinlay et al., 17 Sep 2025, Rane et al., 2023).
• Dynamic and Context-Dependent Alignment: Value alignment must accommodate the evolution of values and situational demands, requiring dynamic and adaptive methods rather than static rules (Korecki et al., 2023, Corrêa, 16 Jun 2024).

7. Summary and Research Outlook

Value alignment is an ongoing process, driven by increasing autonomy and unpredictability of AI systems, political and cultural centralization risks, and the imperative to prevent harms that arise from misalignment. Approaches range from mimetic to anchored to hybrid, each reflecting different trade-offs in empirical robustness, normative legitimacy, and practicality. Aggregation strategies, iterative verification, participatory methods, and interdisciplinary frameworks characterize the evolving methodology. Central challenges include managing plural values, operationalizing abstract principles, and implementing adaptive processes that reconcile dynamic and system-level complexities. Future research will continue to integrate ethical theory, technical innovation, and participatory governance to ensure that AI agents implement and reflect the values of diverse human constituencies (McKinlay et al., 17 Sep 2025).