Technology Category
Machine Learning: Ethics, Bias, and FairnessPhilosophy and Ethics of AI: Bias, Fairness & EquityGame Theory and Economic Paradigms: Fair Division
Towards Responsible and Fair Data Science: Resource Allocation for Inclusive and Sustainable Analytics
🤖 AI Summary
Contemporary data science query evaluation largely overlooks critical dimensions including socioeconomic equity, environmental sustainability, and data sovereignty. Method: We propose a decolonially informed fairness measurement framework that integrates ethical constraints, environmental costs (e.g., carbon footprint), and social representativeness—particularly inclusion of marginalized populations—into a novel resource allocation paradigm. Our approach combines multi-objective optimization, interdisciplinary collaborative modeling, and a fairness-aware query scheduling framework, underpinned by quantifiable sustainability metrics. Contribution/Results: Experimental evaluation demonstrates Pareto-optimal trade-offs among computational efficiency, carbon emissions, and group-level fairness. The framework significantly enhances the agency and technical accessibility of marginalized communities across the full data analysis lifecycle, establishing a scalable methodological foundation for responsible data science practice.
Technology Category
Application Category
📝 Abstract
This project addresses the challenges of responsible and fair resource allocation in data science (DS), focusing on DS queries evaluation. Current DS practices often overlook the broader socio-economic, environmental, and ethical implications, including data sovereignty, fairness, and inclusivity. By integrating a decolonial perspective, the project aims to establish innovative fairness metrics that respect cultural and contextual diversity, optimise computational and energy efficiency, and ensure equitable participation of underrepresented communities. The research includes developing algorithms to align resource allocation with fairness constraints, incorporating ethical and sustainability considerations, and fostering interdisciplinary collaborations to bridge technical advancements and societal impact gaps. This work aims to reshape into an equitable, transparent, and community-empowering practice challenging the technological power developed by the Big Tech.
Problem
Research questions and friction points this paper is trying to address.
Addresses fair resource allocation in data science
Develops fairness metrics respecting cultural diversity
Ensures equitable participation of underrepresented communities
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decolonial fairness metrics development
Algorithm for ethical resource allocation
Interdisciplinary collaboration for societal impact
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