User-Driven Value Alignment

• User-driven value alignment is a framework that empowers end users to actively shape AI outputs via formal methods and participatory design.
• It leverages explicit control and reflective elicitation protocols to capture user preferences and trade-offs, improving AI system alignment.
• Algorithmic innovations such as reward optimization, modular aggregation, and interactive verification ensure outputs adhere to personalized values.

User-driven value alignment refers to a diverse set of formal, algorithmic, and participatory frameworks in which end users (and their communities) maintain agency in specifying, refining, and verifying how AI systems map outputs, decisions, and behaviors onto their own values, goals, or interests. In contrast to traditional, designer- or aggregate-driven alignment approaches, user-driven value alignment incorporates the direct feedback, preferences, interventions, or oversight of the humans most affected by the system’s outputs. It spans technical pipelines (reward modeling, optimization, verification), participatory design methods, and empirical studies demonstrating users’ ability to shape or contest the value commitments of deployed AI. It is now a foundational paradigm for aligning LLMs, social media feeds, embodied agents, human-robot teams, and other AI infrastructures to the plurality of human values.

1. Formal Definitions and Core Technical Models

User-driven value alignment is formalized as a dynamic process involving:

• A set of user (or community) values $V=\{v_1,v_2,\ldots\}$, which may be provided directly, inferred through interaction, or elicited through preference modeling.
• A space of AI outputs or behaviors $S=\{s_1,s_2,\ldots\}$.
• An alignment predicate $A:S\times V\to\{\text{aligned},\text{misaligned}\}$, indicating for each system output whether it matches the user value profile.
• An intervention operator $I:S\times V\to S'$, capturing user-initiated realignments via feedback, teaching, or configuration, such that subsequent outputs have $\Pr[A(s',V)=\text{aligned}]$ increased (Fan et al., 2024).

Canonical examples:

• In value-driven social media feeds, users directly provide a weight vector $w\in[-1,1]^{19}$ over Schwartz’s values; each post has an LLM-classified value expression $v_p\in\mathbb R^{19}$; the feed is sorted by $s(p)=w\cdot v_p$ (Jahanbakhsh et al., 17 Sep 2025).
• The MAP (Multi-Human-Value Alignment Palette) framework formalizes user-driven alignment via a constrained maximization:

$$\max_x \sum_i w_i r_i(x)\quad\text{subject to}\;\; r_i(x)\geq \tau_i$$

where $r_i(x)$ measures value $S=\{s_1,s_2,\ldots\}$0 in candidate $S=\{s_1,s_2,\ldots\}$1, $S=\{s_1,s_2,\ldots\}$2 are user-specified trade-off weights, and $S=\{s_1,s_2,\ldots\}$3 are minimum acceptable thresholds (Wang et al., 2024).

• In contextual moral value alignment, users—via a “moral profile” vector $S=\{s_1,s_2,\ldots\}$4—drive the aggregation of responses from distinct value-specific LLM agents, generating a single output optimally aligned to $S=\{s_1,s_2,\ldots\}$5 (Dognin et al., 2024).

Across these architectures, the user’s agency is positioned at the “top” (preference elicitation, interface configuration), “middle” (real-time correction/feedback), or “bottom” (post-hoc verification/evaluation). The alignment dynamics are structured to maximize interpretability and maintain fidelity to the user’s value priorities.

2. Interaction Protocols and Participatory Mechanisms

User-driven value alignment fundamentally depends on the design and implementation of channels through which users can specify values, express trade-offs, contest system behavior, and verify value compliance.

Two broad interaction paradigms are prominent:

• Explicit Control: Users are afforded fine-grained controls (e.g., sliders, natural language interfaces, selection of weights/thresholds) to specify the relative prioritization of different values (Jahanbakhsh et al., 17 Sep 2025, Wang et al., 2024). For example, user interfaces may expose each of 19 values via an adjustable slider, with weights updated and system outputs re-ranked in real time.
• Reflective Elicitation: Interactive-Reflective Dialogue Alignment (IRDA) pipelines iteratively present users with diverse agent behaviors/trajectories and explicitly prompt them for both labels (“Does this align?”) and rationales (“Why?”), generating a personalized reward model that captures nuanced, subjective value definitions (Blair et al., 2024).

Participatory design approaches leverage workshops, misalignment diaries, and co-design artifacts to support users in identifying, reflecting on, and negotiating value misalignments; users can assume roles such as “adjuster,” “interpreter,” “refuser,” “curator,” or “steward,” each associated with specific forms of interaction and feedback (Arzberger et al., 22 Jan 2026). The user is thus conceptualized as an epistemic agent, not merely a data source.

3. Algorithmic and Optimization Frameworks

Technical instantiations of user-driven value alignment fall into several classes:

A. Reward and Policy Optimization with User Constraints

• Multi-objective alignment: The MAP framework casts user constraints as hard thresholds in a primal-dual optimization:

$S=\{s_1,s_2,\ldots\}$6

converging to solutions that strictly respect user-imposed minimums and trade-offs (Wang et al., 2024).

B. Aggregation and Modular Decision-Making

• Systems like CMVA-GS maintain a distinct fine-tuned agent for each value dimension. A user’s profile vector $S=\{s_1,s_2,\ldots\}$7 directs an LLM-based aggregator to combine outputs contextually, yielding superior alignment and response metrics (e.g., ROUGE improvement over RLHF or aggregate prompt baselines) (Dognin et al., 2024).

C. Verification and Interactive Evaluation

• Value alignment verification provides users with “driver’s test” protocols: small sets of queries or preference assessments that can certify, up to an $S=\{s_1,s_2,\ldots\}$8-tolerance, whether agent behavior is aligned to their own value model. This approach drastically reduces evaluation burden compared to exhaustive simulation and actively empowers non-experts to test black-box systems (Brown et al., 2020).

D. Stackelberg and Game-Theoretic Analysis

• In engagement-driven systems where user interactions are behavioral (e.g., content consumption signaling), the alignment burden is shaped by user foresight (horizon $S=\{s_1,s_2,\ldots\}$9), with critical thresholds required to steer agent behavior towards true interests. Minimal costly signaling (e.g., feedback buttons) sharply reduces required horizon, making user-driven alignment tractable even with inconsistent preferences (Shirali, 18 Oct 2025).

4. Practical User Interfaces and Feedback Affordances

The translation of user agency into effective alignment hinges on user interface (UI) design. Reference models decompose the feedback loop into stages:

• Data Sampling
• Visual Transformation (highlighting decision-relevant features)
• Interactive Display (side-by-side, hierarchical, or decomposed views)
• User Analysis & Interaction (exploration, drill-down, comparison)
• Structured Feedback Submission (binary preference, ranking, groupwise comparison, correction) (Shi, 12 Feb 2026).

Empirical case studies in UI-mediated alignment (e.g., Decomposed eXplanation or Interactive Groupwise Comparison interfaces) demonstrate that richer feedback affordances not only increase annotation efficiency, but also improve inter-annotator agreement and alignment speed. For instance, collecting groupwise preferences ($A:S\times V\to\{\text{aligned},\text{misaligned}\}$0-way) yields $A:S\times V\to\{\text{aligned},\text{misaligned}\}$1 inferred pairwise labels per annotation, reducing the volume of required queries by 30–50%.

Best practices include surfacing model positionality, supporting value trade-off sliders, legitimate non-engagement, shared annotation libraries, and integration with backend alignment mechanisms. Adaptive UIs can personalize exploration and feedback elicitation based on user expertise or trust levels (Shi, 12 Feb 2026, Arzberger et al., 22 Jan 2026).

5. Empirical Results and Limitations

Experimental investigations confirm that:

• Users reliably distinguish value-aligned outputs/feeds from default or engagement-optimized outputs, with recognizability rates of 63–76% in social media feeds when using explicit controls (Jahanbakhsh et al., 17 Sep 2025).
• Personalized models (e.g., IRDA) lead to significant gains in reward prediction accuracy over language-only or collective baselines, especially in high-heterogeneity, low-sample settings (Blair et al., 2024).
• User-perceived value alignment correlates with both self-reported preference measures and downstream trust or task satisfaction (e.g., in human-robot teaming under high-risk scenarios) (Bhat et al., 2024).
• Direct and context-sensitive alignment—rather than top-down or aggregate-based methods—addresses many previously intractable misalignments in conversational agents and real-world deployments, notably in sensitive or marginalized-user scenarios (Motnikar et al., 26 Jun 2025, Fan et al., 2024).

Limitations of current approaches include sensitivity to reward model misspecification, reduction in recognizability under multi-value trade-off conditions, cultural and LLM-bias in content labeling, cognitive overhead for users with inconsistent preferences, and challenges in generalizing to temporally evolving or context-specific value shifts (Jahanbakhsh et al., 17 Sep 2025, Wang et al., 2024, Shirali, 18 Oct 2025).

6. Integrative and Theoretical Extensions

Foundational work formalizes value aggregation over agents and values, with explicit alignment metrics based on user-specified preference increment functions $A:S\times V\to\{\text{aligned},\text{misaligned}\}$2 evaluated over the state space and (optionally) aggregated via user-chosen social choice or multi-value functions. Monte Carlo techniques approximate alignment of AI norms with user values in normative transition systems (Sierra et al., 2021).

Robust alignment further requires concept alignment: inferring or negotiating the mapping of user and agent conceptual schemas (features, world representations) as a prerequisite for value-alignment—failing to do so leads to systematic errors in inferred values and resulting behavior (Rane et al., 2023). Goal alignment frameworks explicitly model user mental models and asymmetry, iteratively querying users to disambiguate between observably attainable goals and true latent objectives (Mechergui et al., 2023).

Combining bottom-up, empirical ontology-driven misalignment tracking (via logs and adverse effects) with top-down constraints (e.g., legal, ethical guidelines) as a multi-objective optimization problem has been proposed for conversational AI, supporting continuous adaptation of AI behavior to evolving real-world contexts (Motnikar et al., 26 Jun 2025).

7. Future Directions and Open Challenges

Key open technical and methodological challenges include:

• Scaling personalized reward/constraint pipelines to population-scale deployment without loss of signal from minority or marginalized users (Blair et al., 2024).
• Enabling value alignment in multi-agent and multi-user environments with conflicting or dynamically shifting values (Wang et al., 2024, Jahanbakhsh et al., 17 Sep 2025).
• Developing efficient, interpretable surrogate models capable of online, user-facing verification and feedback incorporation (Brown et al., 2020, Dognin et al., 2024).
• Formalizing and evaluating new UI paradigms that support explanatory, correctional, and non-engagement actions (Shi, 12 Feb 2026, Arzberger et al., 22 Jan 2026).
• Integrating concept and value alignment in large, real-world systems with latent or evolving world models (Rane et al., 2023, Mechergui et al., 2023).
• Handling the cognitive burden and supporting users with inconsistent long- and short-term preferences in reinforcing environments (Shirali, 18 Oct 2025).

Continued research is needed to bridge theoretical guarantees with practical participatory and interface design, ensuring that user-driven value alignment remains feasible, interpretable, and robust across the evolving spectrum of AI deployments.