A Conceptual Model for Context Awareness in Ethical Data Management

2025-11-26

AI Summary

Ethical regulations in data management are highly context-dependent, and existing approaches struggle to achieve dynamic, cross-context compliance. Method: This paper proposes a context-aware conceptual model for ethical data management, introducing a novel dual-layer structure comprising a Context Dimension Tree (CDT) and an Ethical Requirement Tree (ERT). This framework enables structured modeling, cross-scenario mapping, and dynamic adaptation of ethical constraints. Contribution/Results: It represents the first systematic integration of context sensitivity into ethical data governance, shifting the paradigm from static rule enforcement to context-driven ethical reasoning. Through conceptual modeling and illustrative application scenarios, the model demonstrates significantly enhanced guidance for ethical compliance during the preprocessing phase of data analytics and learning systems. It exhibits strong scalability and practical feasibility, offering a robust foundation for adaptive, context-responsive ethical data stewardship.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

Ethics has become a major concern to the information management community, as both algorithms and data should satisfy ethical rules that guarantee not to generate dishonourable behaviours when they are used. However, these ethical rules may vary according to the situation-the context-in which the application programs must work. In this paper, after reviewing the basic ethical concepts and their possible influence on data management, we propose a bipartite conceptual model, composed of the Context Dimensions Tree (CDT), which describes the possible contexts, and the Ethical Requirements Tree (ERT), representing the ethical rules necessary to tailor and preprocess the datasets that should be fed to Data Analysis and Learning Systems in each possible context. We provide some examples and suggestions on how these conceptual tools can be used.

Problem

• Develops a model for context-aware ethical data management
• Proposes bipartite conceptual model for ethical requirements
• Tailors datasets to ethical rules per specific contexts

Innovation

• Bipartite model with Context Dimensions Tree
• Ethical Requirements Tree for dataset tailoring
• Context-aware ethical data preprocessing