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Human-in-the-Loop AI: Rethinking Automation Ethics in Decision-Sensitive Domains Case Study of the Education, IT and Non-for-Profit sectors
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This study develops and applies the Human-in-the-Loop (HITL) Ethical Assessment Framework (EHAF) to examine the ethical sufficiency of HITL artificial intelligence (AI) across education, information technology (IT), and non-profit sectors. The research objective was to evaluate how effectively HITL practices safeguard human values in decision-sensitive contexts and to identify sector-specific challenges that may compromise ethical adequacy. Adopting a qualitative thematic approach, we analyzed survey responses from professionals in the three sectors. Responses were coded against the four diagnostic dimensions of EHAF Impact Severity, Contextual Ambiguity, Human Agency, and Transparency & Auditing while also allowing for the identification of emergent themes. Retroductive reasoning was used to move beyond surface patterns to uncover generative mechanisms shaping HITL practices. Findings demonstrate sectoral variation in how HITL systems are operationalized and valued. In education, ethical sufficiency is closely tied to human oversight given the high stakes of student outcomes and the importance of cultural contextualization. In the non-profit sector, transparency and auditing dominate due to donor accountability pressures and reporting requirements. IT organizations, by contrast, privilege efficiency and scalability, but often provide weaker safeguards for human agency and oversight. Across all sectors, emergent themes such as training, trust, infrastructure readiness, and donor influence were found to condition HITL adequacy. Generative mechanisms identified include institutional role ambiguity, donor pressure, cultural misalignment, and capacity constraints. The study concludes by proposing an extension to EHAF that incorporates a fifth dimension, Capacity and Governance Context to better capture systemic and institutional influences. Conceptually, the paper refines the assessment of HITL ethics, while practically offering sector-specific recommendations to strengthen oversight, accountability, and trust in AI-enabled decision-making.
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“A coherent HITL bias-mitigation strategy therefore requires dual-level evaluation: case-level review quality and outcome-level disparity monitoring over time, with escalation triggers when the two diverge [68].”
From Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications (2026) · cites this paper as [68]
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“Relying solely on human oversight to review decisions made on a case-by-case basis may not be sufficient to point to biases that only become apparent when outcomes are reviewed on a wide range of decisions.”
From Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications (2026) · cites this paper as [68]
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“The evaluation of HITL systems requires a coherent framework that treats performance as a joint property of humans, AI models, interfaces, and organizational context rather than as model accuracy alone.”
From Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications (2026) · cites this paper as [68]
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“They found that while biases are small individually, their aggregated effect can be substantial [68].”
From Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications (2026) · cites this paper as [68]
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Metadata
- Authors
- Joseph, N. S., & Yakubu, M. N.
- Affiliations
- Arden University (GB)
- Venue
- International Journal of Research and Innovation in Applied Science
- Publisher
- RSIS International
- Published
- 2025-10-30
- Volume
- 10
- Issue
- 10
- Pages
- 361-372
- ISSN
- 2454-6194 (ISSN-L)
- OpenAlex
- W4415741572
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 3
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Research and Innovation in Applied Science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Ethics in Business and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- human-in-the-loop AI, AI ethics, ethics of automation, human oversight, human agency, education sector, IT industry, third sector, donor accountability, trust in AI, AI decision-making
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- CoChat. (2026). Citation check: Human-in-the-Loop AI: Rethinking Automation Ethics in Decision-Sensitive Domains Case Study of the Education, IT and Non-for-Profit sectors. https://app.cochat.ai/papers/10.51584/ijrias.2025.1010000027
- The paper
- Joseph, N. S., & Yakubu, M. N. (2025). Human-in-the-Loop AI: Rethinking Automation Ethics in Decision-Sensitive Domains Case Study of the Education, IT and Non-for-Profit sectors. International Journal of Research and Innovation in Applied Science. https://doi.org/10.51584/ijrias.2025.1010000027
