Human-in-the-Loop AI: Rethinking Automation Ethics in Decision-Sensitive Domains Case Study of the Education, IT and Non-for-Profit sectors

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.