WSEAS Transactions on Computer Research
Print ISSN: 1991-8755, E-ISSN: 2415-1521
Volume 14, 2026
Responsible Machine Learning Deployment: Imperative Framework for Ethical Action
Author:
Abstract: The rapid expansion of Machine Learning (ML) across finance, healthcare, education, and public policy
makes ethical oversight an imperative rather than an optional add-on. This paper responds to that urgency by
proposing a comprehensive framework grounded in ten principles—accuracy, fairness, accessibility, security,
privacy, transparency, accountability, human oversight, sustainability, and harm avoidance—and positioning
them within existing international guidelines. Recent scoping reviews have highlighted the lack of consistent
evaluation frameworks across domains and have called for systematic approaches to fairness, accountability,
transparency, and ethics. Motivated by case studies of algorithmic redlining, dataset bias, hallucinations in
large language models, and ecological concerns, we develop a weighted scoring rubric with thresholds to
diagnose ethical compliance. We demonstrate the rubric through case studies, illustrating how the scores identify
deficiencies and guide mitigation. The proposed framework is built upon the EU AI Act, NIST’s AI Risk
Management Framework, UNESCO’s recommendations, and the OECD AI Principles. We reflect on AI’s energy
footprint and the so-called “nuclear dependence” argument, and conclude with a roadmap for practitioners.
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Keywords: Machine learning, ethical computing principles, fairness, bias mitigation, accessibility,
sustainability, energy and water consumption, scoring rubric
Pages: 141-153
DOI: 10.37394/232018.2026.14.12