Identity and Value Negotiation in AI

• Identity and value negotiation is the ongoing process where individuals and groups redefine self-conceptions and core values in response to evolving technological and social environments.
• Empirical studies reveal structured phase transitions and measurable attitude shifts in creative and ethical perspectives, highlighting the impact of AI on identity formation.
• Formal frameworks like IRDA, negotiative alignment, and PSRO provide algorithmic methods to preserve minority identities and ensure fair multi-agent value consensus.

Identity and value negotiation refers to the dynamic, ongoing process by which individuals and groups reappraise, redefine, and contest their self-conceptions and core values—such as authorship, originality, cultural norms, and fairness—in response to evolving technological, social, and institutional environments. In contemporary AI and human-computer interaction research, this topic has gained substantial prominence with the rise of generative models, value-alignment systems, and pluralistic multi-agent frameworks, all of which foreground the challenge of representing, mediating, and preserving heterogeneous identities and value systems during collective or collaborative decision-making.

1. Theoretical Frameworks of Identity and Value Negotiation

Recent empirical and methodological work has reframed technology adoption as a continuous negotiation process, moving beyond binary acceptance/rejection paradigms. Meng et al. (Meng et al., 5 Nov 2025) introduced “identity and value negotiation” as a theoretical lens for tracing how creators—specifically 17 Chinese digital painters—engage with generative AI over time. Technology adoption is formally treated as a state evolution governed by:

$$\text{Identity}_{t+1} = f(\text{Identity}_t,\, \text{Value}_t,\, \text{AI}_t,\, \text{Peers}_t,\, \text{Emotion}_t)$$

$$\text{Value}_{t+1} = g(\text{Value}_t,\, \text{Identity}_t,\, \text{AI}_t,\, \text{Copyright}_t,\, \text{Labor}_t)$$

where variables represent professional identity, evaluative beliefs, shifting technological affordances, social norms, affective states, and external constraints.

In value-alignment research (Blair et al., 29 Oct 2024), Interactive-Reflective Dialogue Alignment (IRDA) explicitly operationalizes negotiation over values at both the individual and group levels, using iterative elicitation and refinement to tie reward models directly to evolving identity constructs.

Negotiative alignment (Mushkani et al., 16 Mar 2025) and game-theoretic cross-cultural consensus (Zhang et al., 16 Jun 2025) abstract identity negotiation to multi-agent preference optimization, incorporating mechanisms for tracking, preserving, and adaptively weighting unique stakeholder perspectives.

2. Empirical Case Studies and Dynamic Trajectories

Meng et al. (Meng et al., 5 Nov 2025) mapped digital artists’ negotiation process in three stages:

Phase	Dominant Identity	Quantitative Shift	Negotiation Outcome
Resistance (2021–22)	Manual skill, human act	Attitude M: 2.18→1.20 (p=.031)	Identity tied to ontological skepticism, fear of loss
Pragmatic Adoption (2022–24)	Tool-user, operator	Attitude M: 1.20→8.07 (p<.001)	Value shifts to efficiency, hybrid practices
Reflective Reconstruction	Human-in-collaboration	Attitude M: 8.07→7.57 (p=.099), Ethics ≈9.57	Negotiated balance: selective use, authorship preserved

These empirical findings demonstrate that identity and value negotiation is non-monotonic and subject to external pressures (peer norms, compensation, emotional states), persistent ethical concerns, and iterative renegotiation of creative practice.

Urban studies using negotiative alignment (Mushkani et al., 16 Mar 2025) similarly revealed that systematic group disagreements (wheelchair users, seniors, LGBTQIA2+) persist across design tasks, demanding explicit mechanisms to preserve, rather than suppress, minority viewpoints.

3. Formal and Algorithmic Mechanisms

Identity and value negotiation in AI-driven consensus systems requires formalization of preference, negotiation, and identity-preservation dynamics. Key frameworks include:

IRDA Mechanism (Blair et al., 29 Oct 2024)

Mono-user reward inference:

• Feedback dataset: $$\mathcal{D}_i = \{(\tau^{(j)},\, r^{(j)}_i,\, h^{(j)}_i)\}$$
• Reward model: $R(\tau | \theta_i)$, posterior update via

$$P(\theta_i | \mathcal{D}_i) \propto P_0(\theta_i) \prod_j P(r^{(j)}_i | \tau^{(j)}, \theta_i)$$

• Policy optimization: $$\pi_i^* = \arg\max_{\pi} \mathbb{E}_{\tau \sim \pi}[R(\tau | \theta_i^*)]$$

Multi-user aggregation:

• Either maximin or weighted-sum $R_{\text{group}}$ to guarantee minority feature retention.

Negotiative Alignment (Mushkani et al., 16 Mar 2025)

Multi-agent iterative bargaining:

• Consensus score: $x^*(t) = \sum_g \lambda_g(t) P_g(x)$
• Disagreement: $\Delta_g(t) = |P_g(x^*(t)) - x^*(t)|$
• Weights: $$\lambda_g(t+1) = (1-\gamma)\lambda_g(t) + \gamma\,\hat{\Delta}_g(t)$$
• Identity distribution preservation via Jensen–Shannon divergence on stakeholders’ $\pi_g^0, \pi_g(t)$.

PSRO Game-Theoretic Negotiation (Zhang et al., 16 Jun 2025)

Agents encode value-guidelines, negotiate via Nash-equilibrium meta-strategy, and propose best-response expansions. Utility function for agent $i$:

$$u_i(\pi_1,\ldots,\pi_n) = \alpha \cdot \operatorname{sim}(E(\pi_i),E(\pi_i^0)) + \beta \cdot \frac{1}{n-1}\sum_{j\neq i}\operatorname{sim}(E(\pi_i),E(\pi_j)) + \gamma \cdot \text{Novelty}(\pi_i)$$

Policy updates and data transformation anchor identities in regional/cultural priors.

4. Evaluation Metrics and Coding Schemes

Quantitative measures enable rigorous tracking and assessment of negotiation processes:

• Longitudinal survey metrics (Meng et al., 5 Nov 2025): 12-core items (attitude, aesthetics, ethics, reverse-coded concerns), paired t-tests, ANOVA, trend analysis.
• IRDA reward-model accuracy (Blair et al., 29 Oct 2024): Balanced accuracy, Jaccard feature overlap, Fleiss’ κ.
• Group fairness/disagreement (Mushkani et al., 16 Mar 2025): Disagreement Coverage Ratio (DCR), Negotiation Progress (NPM), Identity Preservation Index (IPI).
• Game-theoretic metrics (Zhang et al., 16 Jun 2025): Perplexity-based Acceptance (PPL-Acc), Values Self-Consistency (VSC), fairness PCA projections.
• Qualitative: Thematic coding with >80% inter-coder reliability, case files for narrative evolution, module-level feature preservation.

All approaches foreground the necessity of explicit metrics that document minority representation, identity drift, and feature overlap, enabling transparent audit trails in multi-stakeholder negotiation.

5. Preservation and Negotiation of Minority Identities and Values

Classic averaging or consensus-based aggregation methods risk erasing non-hegemonic identities. The IRDA procedure safeguards personalization by retaining distinct reward models for each participant, allowing explicit Pareto or maximin group aggregation and fairness regularizers. Negotiative alignment dynamically increases group weights for persistent dissenters, using identity-distribution blending to prevent the dilution of marginalized claims.

The PSRO framework (Zhang et al., 16 Jun 2025) embeds each agent’s core values, enabling Nash-equilibrium consensus that does not require assimilation of minority culture into majority norms. This is empirically confirmed by balanced compromise and WEIRD-bias mitigation. Across urban, creative, and value-alignment domains, sustained attention to minority identity is achieved via modular preservation, multi-agent bargaining, and targeted optimization constraints.

6. Design Principles and Implications for AI Systems

Several actionable design strategies arise from these frameworks and empirical studies:

1. Preserve Agency: Avoid opaque, full-automation models; expose interpretable parameters and data provenance to assert user/creator identity (Meng et al., 5 Nov 2025).
2. Layered Collaboration: Support both inspiration and granular control; demarcate which product components are human-made versus AI-generated.
3. Embrace Serendipity and Controlled Failure: Offer exploration and adjustment of AI “errors” to foster creative negotiation (Meng et al., 5 Nov 2025).
4. Make Ethical Metadata Visible: Integrate copyright and bias tags; enable artists and users to negotiate acceptable dataset usage.
5. Emotional Resilience and Community Norms: Visualize peer patterns, trigger fatigue alerts, preserve individual pace in value negotiation (Meng et al., 5 Nov 2025).
6. Multi-agent Negotiation and Minority Protection: Use dynamic weight updates, maximin reward aggregation, and fairness regularization to enforce identity integrity (Mushkani et al., 16 Mar 2025Blair et al., 29 Oct 2024Zhang et al., 16 Jun 2025).

These principles collectively ensure that collaborative AI systems scaffold ongoing identity and value negotiation rather than fixing stakeholders to static, monolithic roles or priorities.

7. Broader Implications and Extensions

Identity and value negotiation frameworks generalize across domains involving heterogeneous stakeholder preferences with non-i.i.d. distributions—from creative labor to urban design, autonomous policy planning, and LLM consensus-building. Dynamic negotiation operators, modular identity preservation, and explicit disagreement metrics enable pluralistic, fairer outcomes and transparent alignment processes.

A plausible implication is that future AI system design must structurally embed negotiation mechanisms at all layers—reward modeling, consensus computation, ethical metadata, and user interface—to preserve, represent, and balance identity plurality. This suggests a transition from one-time value alignment or policy optimization toward continuous, explicit scaffolding of negotiation, ensuring accountability and responsiveness to changing stakeholder concerns.