Principles for Deploying Responsible Machine Learning Models

AUTHOR(S):

Maikel Leon

TITLE

Principles for Deploying Responsible Machine Learning Models

ABSTRACT

This paper examines the ethical foundations that guide the responsible creation and deployment of Machine Learning (ML). Given how rapidly ML is gaining influence in healthcare, finance, and public policy, it is increasingly vital to uphold applications that promote transparency and societal benefit. We highlight ten core principles— accuracy, bias, accessibility, security, privacy, transparency, accountability, human oversight, sustainability, and harm avoidance—and illustrate ways to implement them so that ML systems strengthen social well-being rather than undermine it. Drawing on theoretical perspectives alongside real-world illustrations, we outline best practices that foster trust and responsible progress in ML. Ultimately, we argue that robust governance structures guided by these principles will help steer ML-based projects to become genuine engines for positive social change.

KEYWORDS

Ethical Frameworks, Machine Learning Bias, AI Transparency, Data Privacy, Sustainability in AI, Human-Centric AI Control

Cite this paper

Maikel Leon. (2025) Principles for Deploying Responsible Machine Learning Models. International Journal of Computers, 10, 161-171