Human-in-the-Loop (HITL) in AI-Assisted Disclosure

Authors

  • Fatima Salman Department of Law, University of Punjab, Jhelum Campus Jhelum, Pakistan

DOI:

https://doi.org/10.69971/dss.3.1.2026.46

Keywords:

AI governance, human-in-the-loop, explainable AI, auditability, regulatory disclosure, accountability

Abstract

Artificial Intelligence (AI) has become an integral part of corporate disclosure processes, changing the way that companies create, validate and communicate information to others. The use of AI in decision making and disclosure processes, has revolutionized the governance, in the legal, financial and public administration sectors. Its critical, especially in high-stakes settings such as financial reporting and legal disclosure requirements. Although AI-assisted disclosure is advantageous in terms of efficiency, scalability and analytical capabilities, it poses governance, auditability and explainability challenges. These dimensions are interdependent and current study explores these dimensions highlighting the need for accountability and compliance of human-in-the-loop (HITL) supervision. The study suggests an integrated architecture which includes governance structures, audit trails, explainability techniques and structured human monitoring. Current research analyzes disclosure processes and introduces a new AI-assisted disclosure approach with a Human-in-the-Loop (HITL) system that allows for human oversight, accountability, and trust. EU AI Act, NIST AI RMF, and emerging compliance and open governance practices, should be reviewed regarding their application to HITL systems to investigate trends and patterns in managing ‘black box' risks, audit trails and meaningful explanations. Technical (e.g., XAI techniques such as SHAP/LIME) and procedural safeguards must be combined to provide an effective governance process, holistic HITL supplemented by technical safeguards do not rubber stamp decisions, but instead increase decision quality. Good governance needs to be multi-layered in which there is structured human intervention, regulatory control and technical transparency. A full package approach, combining audit trails, explainability protocols and human review processes is recommended to ensure legally compliant and ethically responsible disclosure when made by AI.

Downloads

Download data is not yet available.

References

Ahmed, Fazail Asrar, Seema Gul, and Salman Shahzad. 2025. Ensuring Accountability and Transparency in AI-Driven Corpo-rate Governance. International Journal of Social Sciences Bulletin 3:330-341. https://doi.org/10.5281/zenodo.15378921

Amershi, Saleema, Maya Cakmak, W. Bradley Knox, and Todd Kulesza. 2014. Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine 35 (4): 105–120. https://doi.org/10.1609/aimag.v35i4.2513

Anomah, Sampson. 2026. Bridging the Interpretability Gap: Integrating Explainable AI into Public Sector Audit Systems in Emerging Economies. Accounting Research Journal 39: 1-27. https://doi.org/10.1108/ARJ-08-2025-0302

Arrieta, Alejandro Barredo, Natalia Díaz-Rodríguez and Javier Del Ser, drien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. 2020. Explainable Arti-ficial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Information Fusion 58: 82–115. https://doi.org/10.1016/j.inffus.2019.12.012

Barocas, Solon, and Andrew D. Selbst. 2016. Big Data’s Disparate Impact. California Law Review 104: 671–732. https://doi.org/10.15779/Z38BG31

Busuioc, Madalina. 2021. Accountable Artificial Intelligence: Holding Algorithms to Account. Public Administration Review 81: 825–836. https://doi.org/10.1111/puar.13293.

Databricks. 2026. AI Governance Best Practices: Responsible and Effective AI Programs. Databricks. https://www.databricks.com/blog/ai-governance-best-practices-how-build-responsible-and-effective-ai-programs

Floridi, Luciano, Josh Cowls, Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca Rossi, Burkhard Schafer, Peggy Valcke, and Effy Vayena. 2018. AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendation. Minds and Machines 28: 689–707. https://doi.org/10.1007/s11023-018-9482-5.

Freeman, R. Edward. 2015. Strategic Management: A Stakeholder Approach. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139192675

Golubovskij, Vladislav. 2024. Explainability and Transparency of AI in Auditing. Bachelor's thesis, University of Twente. https://essay.utwente.nl/106877/.

Herrera-Poyatos, Andrés, Javier Del Ser, Marcos López de Prado, and Jesús L. Lobo. 2026. A Framework for Responsible AI Systems. Arxiv. https://arxiv.org/abs/2503.04739

Jensen, Michael C., and William H. Meckling. 1976. Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics 3: 305–360. https://doi.org/10.1016/0304-405X(76)90026-X .

Jobin, Anna, Marcello Ienca, and Effy Vayena. 2019. The Global Landscape of AI Ethics Guidelines. Nature Machine Intelli-gence 1: 389–399. https://doi.org/10.1038/s42256-019-0088-2.

Karanxha, Giulia, and Paulinus Ofem. 2025. Evaluating Transparency in AI Systems. IEEE International Conference Proceed-ings. IEEE. https://doi.org/10.1109/ICAI60593.2025.11330146 .

Kirk, Marcus, Peter Molk, and Evan Pondel. 2025. AI in Corporate Disclosure. University of Florida Levin College of Law Re-search Paper 26. https://doi.org/10.2308/HORIZONS-2024-074

Lazaros, Konstantinos, Aristidis G. Vrahatis and Sotiris Kotsiantis. 2026. Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications. Entropy 28 (4): 377. https://doi.org/10.3390/e28040377

Mittelstadt, Brent Danial, Patrick Allo, and Luciano Floridi. 2016. The Ethics of Algorithms: Mapping the Debate. Big Data & Society 3: 1–21. https://doi.org/10.1177/2053951716679679

Molnar, Christoph. 2024. Interpretable Machine Learning. 3rd Ed. https://leanpub.com/interpretable-machine-learning

OECD. 2019. OECD AI Principles overview. https://oecd.ai/en/ai-principles

OpenAI. 2023. GPT-4 Technical Report. https://cdn.openai.com› papers

Raji, Inioluwa Deborah, Andrew Smart, and Rebecca N. White et al. 2020. Closing the AI Accountability Gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the ACM Digital Library. https://dl.acm.org/doi/10.1145/3351095.3372873

Rudin, Cynthia. 2019. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence 1: 206–215. https://doi.org/10.1038/s42256-019-0048-x.

Suchman, Mark C. 1995. Managing Legitimacy: Strategic and Institutional Approaches. Academy of Management Review 20: 571–610. https://doi.org/10.5465/AMR.1995.9508080331

Verma, Himanshu, Kirtan Padh, and Eva Thelisson. 2025. Can AI Be Auditable? Arxiv. https://arxiv.org/abs/2509.00575

Downloads

Published

2026-07-05

Issue

Section

Articles

How to Cite

Salman, Fatima. 2026. “Human-in-the-Loop (HITL) in AI-Assisted Disclosure”. Digital Social Sciences 3 (1): 1-4. https://doi.org/10.69971/dss.3.1.2026.46.