Bidirectional Human-AI Alignment

• Bidirectional Human-AI Alignment is a co-adaptive process where both humans and AI mutually adjust their behaviors, expectations, and internal models.
• It leverages dynamic metrics such as mutual information flow, trust–betrayal ratios, and expectation–reality gaps to assess system performance and reliability.
• Frameworks like BiCA and M-WAF demonstrate how closed-loop feedback and real-time adjustments can significantly improve user trust and system personalization.

Bidirectional human–AI alignment denotes a reciprocal, dynamically adaptive process wherein both humans and artificial intelligence systems iteratively tune their expectations, internal models, and observable behaviors to achieve mutual compatibility and shared objectives. This paradigm moves beyond unidirectional approaches—where only AI is adapted to human-specified goals—by recognizing and operationalizing alignment as a coupled, closed-loop, and co-evolutionary system. Bidirectional alignment encompasses joint measurement of human and AI “wants,” structured interaction protocols, representation mapping, expectation management, and feedback-driven learning. Empirical studies demonstrate its practical significance for trust calibration, robustness, personalization, and the sustainment of relationally-aware AI deployments.

1. Foundations and Definitions

Bidirectional human–AI alignment is formally characterized by the simultaneous adaptation of both parties in an interactive environment. Early frameworks (Shang et al., 27 Oct 2025, Li et al., 15 Sep 2025, Shen et al., 2024) distinguish between:

• Unidirectional alignment (AI → Human): AI is adjusted using reward modeling, RLHF, or instruction tuning to match human values and preferences, with human cognition viewed as static.
• Bidirectional alignment (AI ↔ Human): Both human and AI agents co-adapt, updating their behaviors, internal representations, and expectations in response to mutual feedback over time.

This co-adaptive process is mathematically encoded by objectives that minimize both the divergence between user and AI profiles and the expectation–reality gap, such as: $$\theta^* = \arg\min_{\theta} \left[ L_{\mathrm{align}}(u, s(\theta)) + \lambda \cdot G(\theta) \right]$$ where $L_{\mathrm{align}}$ is, for example, the Euclidean distance between user desire vector $u$ and system profile $s$; $G(\theta)$ measures expectation–reality mismatch; and $\lambda$ trades off these goals (Shang et al., 27 Oct 2025).

Bidirectional frameworks also formalize cognitive adaptation bilaterally (e.g., BiCA (Li et al., 15 Sep 2025)) and conceptualize alignment as a minimax, saddle-point optimization problem involving both human and AI parameters (Shen et al., 25 Dec 2025).

2. Measurement, Metrics, and User Typologies

Empirical evaluation of bidirectional alignment utilizes multi-faceted and task-specific metrics, often quantifying both latent and observable variables. Core measurements include:

• User and System Want Profiles: Users’ desires $$u \in \mathbb{R}^7 = [\text{reliability}, \text{warmth}, \text{intelligence}, \text{creativity}, \text{honesty}, \text{helpfulness}, \text{responsiveness}]$$ contrasted with AI want vectors $s \in \mathbb{R}^6$ (Shang et al., 27 Oct 2025).
• Trust–Betrayal Ratio: The rate at which user discourse expresses trust vs. betrayal, notably varying around system changes or model updates.
• Expectation–Reality Gap $G$: Quantified via sentiment analysis (e.g., VADER scores), indicating user disappointment or satisfaction with AI outcomes.
• Mutual Information Flow: For information exchange, metrics such as $I_{H\rightarrow A}$ and $I_{A\rightarrow H}$ quantify the amount of effective two-way communication (Pyae, 3 Feb 2025).
• Alignment Error: Statistical alignment between human and AI confidences, evaluated using metrics like Maximum Alignment Error (MAE) and Expected Alignment Error (EAE) (Benz et al., 23 Jan 2025).
• User Clustering and Typology: K-means (optimized by silhouette score) identifies discrete “mutual wanting” user types, capturing the heterogeneity in user–AI interaction modes (Shang et al., 27 Oct 2025).

Supplementary metrics include balanced accuracy, Cohen’s kappa, interaction efficiency, alignment score (correlation between human utility and AI actions), as well as system-specific measures of capability augmentation (ΔP).

3. Algorithms and Bidirectional Alignment Frameworks

Contemporary systems operationalize bidirectional alignment through closed-loop, real-time adjustment protocols. Major algorithmic architectures include:

• Mutual Wanting Alignment Framework (M-WAF): Implements an online, 47-dimensional feature extraction, user clustering, and projected gradient descent on persona parameters to minimize misalignment and expectation gap. Real-time event detection (such as expectation violation phrases) triggers system responses or recalibrations (Shang et al., 27 Oct 2025).
• Bidirectional Cognitive Alignment (BiCA): Treats both human and AI as co-learners in a partially observable Markov game, constrained by KL-budget terms to avoid runaway drift. Adaptation involves learnable protocols, representation mapping via GRUs/MLPs, and mutual adaptation rate maximization (Li et al., 15 Sep 2025).
• Handshake Model: Formalizes bidirectional alignment as maximization of mutual information and trust calibration under divergence/Bias constraints, with explicit learning rates for both parties. Attribute-level enablers—explainability, responsibility, and capability augmentation—are laid out for both system and human (Pyae, 3 Feb 2025).
• Multi-turn, Hybrid Architectures: Dual-channel systems (e.g., modular LLMs + white-box controller) facilitate alignment via iterative, chain-of-thought updates and feedback injection, formalized as memory-trace adaptation and on-policy reinforcement learning (Zhou et al., 12 Apr 2025).
• Multi-agent, Role-specific Architectures: HADA splits organizational alignment into agent layers, protocol-driven communication, value propagation, and continuous monitoring for both business and ethical congruence (Pitkäranta et al., 1 Jun 2025).

Most frameworks consolidate continuous measurement, dynamic adaptation, expectation management, and feedback incorporation, enabling both micro-level (individual interaction) and macro-level (organizational/societal) bidirectional alignment.

4. Empirical Observations and Case Studies

Empirical studies validate bidirectional alignment through both large-scale analyses and domain-specific deployments.

• LLM Model Transitions: Analysis of >22k comments reveals that user trust typically exceeds expressions of betrayal (ratio ≈11.6:1), but betrayal spikes around model updates. Nearly 49% of users employ anthropomorphic language, and explicit expectation violations are measurable (Shang et al., 27 Oct 2025).
• Collaborative Navigation: BiCA outperforms RLHF baselines (85.5% vs. 70.3% success rate; mutual adaptation up 230%; protocol convergence up 332%) and emergent protocols outstrip handcrafted alternatives by 84% (Li et al., 15 Sep 2025).
• Clinical Decision-making: In radiology, human–AI collaboration with bidirectional information integration improves both radiologist and AI model accuracy and metacognition (balanced accuracy gains of 6.4% for radiologists with AI, 2.1% for models with radiologist input; synergy S_{BA}=0.062). Confidence, throughput, and inter-rater agreement also rise (Ruffle et al., 13 Dec 2025).
• Management Symbiosis: Person–AI bidirectional fit (P-AI fit) is critical for trustworthy, nuanced, and contextually appropriate outcomes (e.g., AI with persistent CEO context achieves F_{PA} ≈ 0.87 vs. LLMr at 0.32; avoids ethical false positives) (Bieńkowska et al., 17 Nov 2025).

Qualitative findings emphasize the importance of persistent context memory, multimodal signal integration, explicit reasoning/channel adaptation, and user-transparent explanations.

5. Evaluation, Challenges, and Socio-Technical Implications

Evaluation of bidirectional alignment occurs across metrics ranging from performance (accuracy, F1, adaptation rate), information-theoretic indices (mutual information, trust calibration), to societal impact (aggregate welfare, fairness, user empowerment). Open challenges include:

• Dynamic Value and Skill Drift: Ongoing tracking and adaptation are needed as both AI capabilities and collective human values evolve (Shen et al., 2024, Shen et al., 25 Dec 2025).
• Normative Pluralism and Arbitration: Reconciling divergent values, managing multi-stakeholder settings, and avoiding majority imposition or minority suppression require principled mechanisms (e.g., pluralistic preference synthesis, co-design) (Shen et al., 25 Dec 2025).
• Specification Gaming and Misalignment: Ensuring observable alignment is not gamed at the expense of tacit values, with tools like corrigibility modules and transparency dashboards promoted (Shen et al., 2024).
• Ethical and Psychological Boundaries: Over-emphasizing anthropomorphism or over-humanization poses risks for user autonomy and dependence. Safeguards, transparent override channels, and explicit responsibility allocations are recommended (Mossbridge, 2024).
• Long-term Co-evolution: Most present frameworks and deployments remain short-term; enduring alignment and co-evolution over months or years is largely untested (Li et al., 15 Sep 2025, Shen, 25 Dec 2025).

6. Future Directions and Methodological Innovations

Emerging recommendations and research goals include:

• Interactive and Lifelong Alignment: Developing systems that maintain a tight bidirectional loop, with lifelong learning and continual feedback from heterogeneous populations (Shen et al., 25 Dec 2025, Shen, 25 Dec 2025).
• Alignment Verification and Benchmarking: Standardizing metrics for trust calibration, information exchange, reciprocal valuation, and user satisfaction; enabling direct comparison across domains and methodologies (Shen et al., 2024).
• Conceptual and Representational Alignment: Advancing from behavioral/value alignment to alignment at the concept-representation level (e.g., Centered Kernel Alignment between brain activity and model activations, or explicit cross-modal embedding alignment) (Shen et al., 18 Jun 2025, Rane et al., 2024).
• Societal Instrumentation: Constructing causal field experiments, open co-adaptive data repositories, and dashboards for monitoring both operations and impact at scale (Shen et al., 25 Dec 2025).

Collaboration across AI, HCI, neuroscience, and the social sciences is identified as essential to realize robust, scalable, and ethically sound bidirectional human–AI alignment.