Human-in-the-Loop Review in AI

Updated 19 August 2025

Human-in-the-loop review is a paradigm where human oversight and expertise are integrated into AI pipelines for improved fairness, explainability, and decision quality.
It enables iterative quality assurance and attribution mechanisms, balancing automated outputs with human corrections across diverse domains such as medicine and robotics.
The approach reduces risks of automation errors and biases while ensuring ethical oversight and adaptive performance in complex, real-world scenarios.

Human-in-the-loop (HITL) review refers to a class of methodologies, system architectures, and sociotechnical frameworks in which human agency remains an integral component within AI or automated decision-making pipelines. The role of the human ranges from providing domain knowledge, correcting machine outputs, offering interpretability, and safeguarding ethical standards, to counterbalancing the risks of automation. HITL review has been invoked across domains such as automated knowledge extraction, system development, robotics, medical decision support, data synthesis, and scientific knowledge organization, with increasing emphasis on fairness, explainability, efficiency, and sociotechnical alignment.

1. Definitions and Underlying Paradigms

HITL review is broadly defined as a paradigm where humans actively participate in at least one phase of an AI- or ML-powered pipeline, serving as reviewers, validators, correctors, or supervisors. The paradigm is distinguished from fully-automated systems by its explicit maintenance of a "closed loop" involving human feedback or intervention (Zanzotto, 2017, Wang et al., 2022). Key variants include:

Decision Review: Human operators review and, if necessary, override machine-generated outputs, as in medical imaging or satellite operations (Budd et al., 2019, Heinrich et al., 2021).
Knowledge Attribution Review: Humans are kept "in the loop" to ensure the traceability of the decision pipeline to original knowledge producers, with mechanisms proposed for fair redistribution of revenue according to contributed data (Zanzotto, 2017).
Iterative Quality Assurance: Human annotators inspect, correct, and refine AI outputs in cycles, especially where model outputs serve as draft, preliminary, or proposed actions, as in systematic literature review or automated software agents (Schroeder et al., 21 Jan 2025, Takerngsaksiri et al., 19 Nov 2024).
Continuous System Evaluation: Human experts or end-users continuously provide feedback and corrections, supporting model retraining and adaptation (Wang et al., 2021, Wu et al., 2021).

The boundary between "human-in-the-loop" and “AI-in-the-loop” ( $AI^2L$ ) is being actively scrutinized. In $AI^2L$ systems, the human remains the primary decision-maker, with the AI as a decision support tool; in classical HITL, the AI is the principal agent with the human providing occasional corrections (Natarajan et al., 18 Dec 2024).

2. Motivations for HITL Review

The primary rationales for embedding human review into AI/ML pipelines are:

Socio-economic fairness: Addressing the problem of “knowledge theft” in which unremunerated or unaware human data producers underpin AI decisions. HITL mechanisms have been proposed to trace and redistribute value flows, compensating originators of training data (Zanzotto, 2017).
Ethics and risk management: Many applications (e.g., online crowdsourcing, medical diagnosis, robotics) present reputational, financial, or ethical risks that cannot be precomputed or fully mitigated by automation, necessitating continual human oversight (Vepřek et al., 2020, Budd et al., 2019).
Complexity and ambiguity: HITL reviews are critical where ML systems cannot fully handle ambiguity, subjective judgment, semantic nuance, regulatory requirements, or rare events (as in early misinformation detection, legal document review, or satellite control) (Mendes et al., 2022, Heinrich et al., 2021).
Transparency and explainability: Human review closes the semantic gap between machine representations (distributed/tensor-based) and symbolic human reasoning, thus facilitating explainable AI (XAI) and auditability (Zanzotto, 2017, Wang et al., 2022).
Continuous quality improvement: Iterative human review supports data and model quality, allowing dynamic correction, learning from real-world feedback, and improved user trust (Du et al., 2022, John et al., 3 Jun 2025).

3. Review Mechanisms and Architectures

HITL review entails specific mechanisms for integrating human oversight into automated workflows:

Attribution mechanisms: Systems must encode data provenance, trace model decisions to originators, and allocate revenue or credit according to quantifiable contribution, e.g., $R_{d_i} = R × (w_i / \sum_j w_j)$ where $w_i$ derives from explainability models (Zanzotto, 2017).
Active learning: Human review prioritizes the annotation of informative or uncertain examples via mechanisms such as uncertainty sampling ( $x^* = \arg\max_x U(\theta, x)$ ) (Budd et al., 2019).
Iterative review cycles: Systems present intermediate outputs (e.g., extracted facts, revised code snippets, document summaries) to human reviewers for acceptance or correction. Accepted edits are propagated as additional context for subsequent refinement rounds (Du et al., 2022, Takerngsaksiri et al., 19 Nov 2024).
User interfaces and transparency: Visualization interfaces (e.g., model history trees, result timelines, datagrids) enable comparison of alternative hypotheses, highlight changes, and capture branching feedback (Fang et al., 2023, John et al., 3 Jun 2025).
Hybrid expertise integration: HITL review may involve both domain experts and “artificial experts” (specialized ML models trained on human-reviewed unknown classes), with arbitration or selection among experts by mechanisms such as out-of-distribution (OOD) detection and gating networks (Jakubik et al., 2023).

4. Challenges and Open Issues

Despite its efficacy, HITL review introduces major computational and sociotechnical challenges:

Attribution and privacy: Accurately tracing and crediting contributions along the knowledge lifecycle requires robust, privacy-preserving virtual identity protocols and resilient tracking infrastructures (Zanzotto, 2017).
Scalability and efficiency: Reliance on human review for every uncertain or low-confidence instance can severely limit throughput or impose unsustainable resource costs (Jakubik et al., 2023). Solutions involve intelligent allocation between human and artificial experts, and semi-automated review prioritization.
Human judgment noise and bias: Empirical studies show that human reviewers are inconsistent, context-dependent, and subject to cognitive biases (anchoring, loss aversion, framing effects), which may impair convergence or induce suboptimal corrective actions (Ou et al., 2022).
Evaluation and benchmarking: Traditional metrics (accuracy, recall) often fail to capture the holistic efficacy of HITL systems. There is a call for new evaluation protocols that measure human-in-the-loop impact, such as ablation studies separating AI and human contributions, or utility metrics balancing automated accuracy and human cost (Natarajan et al., 18 Dec 2024, Jakubik et al., 2023).
Ethical oversight and governance: Classical IRB processes are not fit for dynamic, distributed, and participatory human computation projects. Evolving, participatory, and “sandboxed” ethical review frameworks are being explored, involving IRB experts directly in project workflows with feedback loops for continuous guideline improvement (Vepřek et al., 2020).

5. Application Domains and Empirical Results

HITL review underpins work in diverse domains, including:

Medical imaging: Human experts prioritize annotations via active learning, refine model outputs using iterative feedback, and calibrate uncertainty for clinical integration (Budd et al., 2019).
Scientific knowledge organization: Modular frameworks leveraging neural models and knowledge graphs, coupled with HITL review, accelerate corpus creation and knowledge extraction; calculated time savings are dramatic (from hours-weeks to sub-hour completion), with reported usability scores of SUS=84.17 (A+) (John et al., 3 Jun 2025).
Data extraction for systematic reviews: LLMs (e.g., Gemini 1.5 Pro) have achieved up to 83.33% “exact match” in extracting explicit variables, but accuracy for derived/categorical variables remains limited, underlining the need for human verification via GUI-assisted review (e.g., AIDE) (Schroeder et al., 21 Jan 2025).
Software development: LLM-based agentic frameworks allow practitioners to review, refine, and approve both planning (file localization) and coding stages. Practitioners found that HITL review reduces initial development time and effort. However, code quality concerns persist, especially for nuanced requirements not captured in unit tests (Takerngsaksiri et al., 19 Nov 2024).
Robotics and control: Interactive planning frameworks using LLM common-sense reasoning correct vision-based plan hallucinations and allow human users (via GUI or voice) to iteratively refine robot behaviors, improving safety and robustness (Merlo et al., 28 Jul 2025).
NLP and misinformation detection: HITL review supports claim extraction, stance classification, and policy violation identification in early misinformation detection, combining automatic trend scoring with manual, guideline-driven validation (Mendes et al., 2022).

6. Future Directions and Framework Evolution

Research in HITL review is rapidly progressing toward more integrated, holistic, and collaborative intelligence paradigms:

Explainability and attribution frameworks: Advances in XAI are sought to precisely decompose model decisions and assign contributory shares back to the human data originators, supporting fair revenue distribution (Zanzotto, 2017).
Holistic system unification: There is a concerted effort to unite active learning, interactive feedback, and robust deployment in end-to-end systems, with pipeline architectures that flexibly adapt to the specifics of domain constraints (Budd et al., 2019).
Human-centric and $AI^2L$ perspectives: The push for $AI^2L$ reframes automated systems as decision support engines, emphatically centering human judgment, with evaluation protocols aligned to usability, transparency, and societal impact rather than just baseline model accuracy (Natarajan et al., 18 Dec 2024).
Crowd and collaboration scale: Comprehensive frameworks for collaborative, peer-based review (rather than solo expert-to-AI paradigms) and the inclusion of collective feedback mechanisms (e.g., branching model histories, discussion boards) are in demand (Wang et al., 2022).
Ethical governance co-evolution: The trend toward dynamic, participatory ethical oversight frameworks aligns system design with evolving societal values, enabling continuous adaptation to emerging regulatory and reputational risks (Vepřek et al., 2020).

7. Summary Table: Core HITL Review Dimensions

Dimension	Example Mechanism	Reported Impact/Challenge
Data Attribution	Explainable AI, provenance logs	Needed for fair credit/revenue allocation and privacy (Zanzotto, 2017)
Efficiency	Expert selection, OOD detection	Hybrid HITL–AIITL systems lower human effort/cost (Jakubik et al., 2023)
Error Mitigation	Iterative GUI review, LLMs	Users correct vision/model errors in cycles (Merlo et al., 28 Jul 2025)
Usability	GUI, model trees, SUS metric	Significant time savings; high usability (SUS = 84+) (John et al., 3 Jun 2025)
Evaluation	Utility score, human–AI ablation	Need for metrics capturing both accuracy and review cost (Natarajan et al., 18 Dec 2024)

In conclusion, HITL review frameworks embody a principled shift toward AI systems that robustly embed, respect, and leverage human expertise, oversight, and rights, both as a source of domain knowledge and as gatekeepers of system trust, fairness, and adaptability. Challenges remain in attribution, quality control, human–AI coordination, and evaluation. Ongoing research emphasizes explainability, aggregation of diverse human contributions, and the institutionalization of continuous, participatory oversight as essential for trustworthy, socially aligned AI.