Human-in-the-loop Feedback

• Human-in-the-loop feedback is an interactive paradigm that integrates human corrections, ratings, and annotations to enhance machine learning outcomes.
• It leverages diverse modalities—from direct corrections and expert ratings to counterfactual demonstrations—to improve model performance and interpretability.
• Empirical evaluations show gains such as a 9 mIoU boost in segmentation and 20–30% cost savings in autonomous control systems.

Human-in-the-loop (HITL) feedback denotes any supervised or interactive machine learning and decision-making paradigm where human users directly supply corrections, ratings, labels, or other forms of input that are systematically integrated into the model’s learning process. The governing principle is the tightly coupled adaptation of models in response to explicit or implicit human judgment, ranging from targeted interventional edits in perception, expertise-driven guidance in anomaly detection, to iterative preference interventions in sequential control. HITL feedback is often formalized as an auxiliary or interventional signal—enabling data efficiency, error localization, model robustness, interpretability, and system alignment with operational or domain-specific human requirements.

1. Mathematical Formalization of HITL Feedback

HITL feedback is typically integrated as an auxiliary signal in the learning objective or policy optimization process. Let $\theta$ denote model parameters, and $D = \{(x_i, y_i)\}$ the canonical labeled dataset. A human feedback set $F = \{(x_j, f_j)\}$, where $f_j$ is a human-supplied signal (correction, scalar preference, reward, annotation, or counterfactual) is introduced. The joint loss is typically

$$L(\theta) = L_{\text{data}}(\theta; D) + \lambda L_{\text{feedback}}(\theta; F)$$

For supervised tasks, $L_{\text{feedback}}$ may consist of a cross-entropy, mean-square error, or other canonical loss term between the model prediction and feedback-derived labels. For reinforcement learning (RL), human feedback may substitute for or augment the environment reward, or be used to learn separate value functions or reward models (Wang et al., 2021, Arakawa et al., 2018).

In segmentation, for instance, human corrections on region $R$ are treated as interventional signals in causal language, formalized as a do-operation:

$$\mathrm{do}\bigl(\mathrm{segmentation}[R] \leftarrow y^*\bigr)$$

where $y^*$ are the human-supplied corrected labels for pixels in $R$ (Shaeri et al., 11 Oct 2025). These interventions are then systematically injected into the loss as

$$\mathcal{L}_{\text{total}}(\theta) = \mathcal{L}_{\text{seg}}(\theta) + \lambda_{\text{cf}} \mathcal{L}_{\text{cf}}(\theta) + \lambda_{\text{prop}} \mathcal{L}_{\text{prop}}(\theta)$$

In RL contexts, COACH/E-COACH-style algorithms update policy parameters via eligibility traces weighted by human feedback, with formal convergence when feedback takes the form of reward, policy, or advantage feedback (Shah et al., 2021).

2. Forms and Modalities of Human Feedback

HITL feedback spans a spectrum of expressivity and granularity:

• Direct corrections: Pixel- or region-wise relabeling (e.g., segmentation masks (Shaeri et al., 11 Oct 2025)), entity resolution adjudications (Yin et al., 2021), object detection bounding-box edits (Honeycutt et al., 2020).
• Expert confidence scores: Continuous ratings, e.g., isFraud scores in [1,100] for financial fraud detection (Kadam, 7 Nov 2024).
• Tagging and qualitative ratings: Discrete feedback tags (e.g., five-level quality assessments in educational AI (Tarun et al., 14 Aug 2025)) or Likert-type psychometric scales (So, 2020).
• Binary/ordinal responses: Complaint (binary), satisfaction (ordinal), policy agreement indicators (Zhou et al., 2020, Arakawa et al., 2018).
• Counterfactual/corrective demonstrations: In policy adaptation, users demonstrate alternative trajectories, label concept changes as task-irrelevant/relevant, or provide counterfactual goal preferences (Peng et al., 2023).
• Implicit signals: Passive brain signals (e.g., fNIRS data mapped to agent performance for implicit feedback (Santaniello et al., 14 Jun 2025)), user dwell time, or reaction patterns.
• Natural language and structured instructions: Free-form corrections or requirements used to shape NLP outputs, or to supply constraints for topic modeling (Wang et al., 2021, Fang et al., 2023).

3. Integration Mechanisms: Propagation, Model Update, and Workflow

A recurrent HITL pattern is the propagation and scaling up of sparse human feedback across the data or task domain:

• Feedback Propagation in Graph and Vision: SME labels in graph-based financial fraud are algorithmically propagated via weighted label smoothing across graph neighbors, using update rules such as

$$S_j^{(h)} = S_j^{(h-1)} + S_i^{(h-1)}\frac{W_{ij}}{\max(W)}\mathrm{Sim}(i,j)$$

to extend expert supervision efficiently (Kadam, 7 Nov 2024).

• Region similarity propagation: Human corrections in image segmentation are spread to visually similar regions based on color and texture descriptor matching, enabling rapid model-wide correction via nearest-neighbor search and embedding agreement (Shaeri et al., 11 Oct 2025).
• Buffering and temporal integration in RL: Human feedback (e.g., occupancy overrides in HVAC control) is retained in a rolling buffer and factored into the RL state; in DQN-TAMER, delayed, stochastic, and binary human inputs are buffered and used to train a separate value function (Liang et al., 9 May 2025, Arakawa et al., 2018).
• Active and intelligent selection: Instances for annotation are selected to maximize informativeness (high uncertainty, model disagreement, or diverse coverage) (Yin et al., 2021). In topic modeling, feedback is injected via multiplicative potential functions within Gibbs sampling (Fang et al., 2023).

In all modalities, feedback is used for (a) direct loss augmentation, (b) alteration/relabeling of training data, or (c) shaping of rewards, policies, or exploration strategies.

4. Applications and Domain-Specific Workflows

HITL feedback has been adopted across a wide spectrum of domains:

• Vision: Interventional human feedback guides the segmentation model away from spurious feature reliance and toward robust, generalizable boundaries; propagation and counterfactual losses substantially improve mIoU and cut annotation cost (Shaeri et al., 11 Oct 2025).
• Autonomous Control and RL: Human override signals and comfort preferences are integrated into PPO-based HVAC systems, yielding 20–30% cost reductions and improved occupant comfort (Liang et al., 9 May 2025). In robotics, strategies like DQN-TAMER and HuGE decouple policy learning from feedback-driven exploration, tolerating sparse, delayed, and noisy labels (Arakawa et al., 2018, Torne et al., 2023).
• Expert Decision Support: Financial fraud systems leverage sparse, high-confidence SME annotations, propagating feedback through GNN layers and graph structure, yielding ~9% AUC lift and major annotation efficiency gains (Kadam, 7 Nov 2024).
• Human-centered AI and Education: Feedback-tagging schemes in adaptive learning systems enable dynamic tailoring of generative AI responses through prompt engineering and live feedback loops (Tarun et al., 14 Aug 2025), while psychometric measures map end-user sentiment to iterative design planning in agile cycles (So, 2020).
• Topic Modeling and Text Analytics: User refinements and constraints in topic modeling are dynamically injected as potentials in the underlying sampler, supporting both global and targeted analyses with tracked model branching and robust UI mechanisms (Fang et al., 2023).
• Entity Resolution: Human feedback rapidly closes distribution gaps by augmenting static model data with targeted labels, closing the performance gap with only modest annotation budgets (Yin et al., 2021).

5. Empirical Outcomes and Quantitative Impact

Quantitative evaluations across domains consistently demonstrate that HITL frameworks drive statistically significant improvements:

• Segmentation: Up to 9 mIoU point improvement (12–15% relative) and 3–4$\times$ annotation efficiency (Shaeri et al., 11 Oct 2025).
• Fraud detection: Aggregated AUC lift >7% per HITL cycle, with further ~2% from propagation (Kadam, 7 Nov 2024).
• HVAC control: 20–30% cost savings and low override (<5%) frequency (Liang et al., 9 May 2025).
• RL and robotics: HITL-guided exploration requires up to 50% fewer labels and accelerates convergence under realistic feedback delay and noise (Arakawa et al., 2018, Torne et al., 2023).
• Topic modeling: HITL-integrated systems produce higher topic coherence (0.445→0.482) and document retrieval precision, with half of user refinements stemming from automated word suggestions (Fang et al., 2023).
• Trust and system perception: Notably, explicit feedback solicitation can decrease end-user trust and perceived accuracy, underscoring the need for interface and feedback-channel design that balances correction and confidence (Honeycutt et al., 2020).

6. Challenges: Bias, Cognitive Noise, and Systemic Limitations

• Human bias and inconsistency: Feedback is subject to cognition-induced phenomena such as anchoring, loss aversion, and cross-session variability, which can stall optimization or introduce contradictory guidance (Ou et al., 2022).
• Propagation of error/benefit: Feedback-propagation schemes are effective in densifying supervision but must be carefully weighted to avoid amplifying label noise (Kadam, 7 Nov 2024).
• Effort saturation and engagement: Tagging fatigue, annotation cost, and the cognitive burden of supplying feedback can limit the scalability of explicit HITL strategies (Tarun et al., 14 Aug 2025).
• Robustness to feedback imperfections: The robustness of RL schemes (e.g., DQN-TAMER, COACH, HuGE) is conditioned on their ability to properly discount, buffer, and disentangle human feedback; algorithms that improperly blend human and environmental reward are provably suboptimal (Shah et al., 2021, Arakawa et al., 2018).
• Trust paradox: Providing correction opportunities can reduce user confidence in deployed models, independent of actual performance gains (Honeycutt et al., 2020).
• Implicit feedback complexity: Approaches leveraging implicit or neural signals (e.g., fNIRS-to-performance mappings) show promise for reducing user burden but face technical challenges related to signal attribution, cross-user transfer, and real-time integration (Santaniello et al., 14 Jun 2025).

7. Design Principles and Future Directions

Emergent best practices and future directions for HITL feedback include:

• Decoupling feedback integration: Separate the use of feedback for exploration/distillation and for direct policy or model learning, reducing the risk of biasing learned policies with noisy or nonstationary signals (Torne et al., 2023).
• Feedback type selection: Advantage-style feedback naturally aligns with unbiased policy gradients in RL; higher-granularity feedback may yield more adaptable, but also noisier, model behavior (Shah et al., 2021).
• Interface and user experience design: Persistent history, context banners, difference-highlighting, and tracked model versions are recommended UI mechanisms for reducing cognitive errors and enabling reversible, transparent model evolution (Ou et al., 2022, Fang et al., 2023).
• Propagative scaling: Use graph propagation, visual similarity, or latent-space mapping to amplify sparse feedback for efficient learning in large-scale or sparse domains (Kadam, 7 Nov 2024, Shaeri et al., 11 Oct 2025).
• Privacy and annotation efficiency: Privacy-preserving feedback (e.g., via differential privacy) and intelligent feedback selection for maximal value per annotation are active areas for scalable deployment (Liang et al., 9 May 2025).

HITL feedback is an evolving, interdisciplinary set of modeling and interaction strategies that, when formalized via explicit mathematical objectives and robust propagation mechanisms, can yield substantial gains in performance, robustness, interpretability, and human alignment across diverse domains. The careful engineering of both the analytical frameworks and the human interfaces remains central to maximizing the potential of HITL systems.