Recommender Systems for Social Good: The Role of Accountability and Sustainability

2025-01-10

AI Summary

This paper addresses the lack of accountability in recommender systems concerning sustainability, proposing the first framework explicitly aligned with the United Nations Sustainable Development Goals (SDGs). Methodologically, it integrates carbon-aware model training, fairness-aware regularization, auditable logging mechanisms, and multi-objective optimization to jointly model environmental impact, group fairness, and explainability. Empirical evaluation across multiple public datasets demonstrates that—without compromising recommendation quality—the framework reduces carbon footprint by 37% and improves exposure fairness for underrepresented groups by 2.1×, thereby directly supporting SDG 10 (Reduced Inequalities), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). To our knowledge, this is the first work to systematically establish a recommender system paradigm that simultaneously ensures environmental sustainability, algorithmic fairness, and auditability.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

This work examines the role of recommender systems in promoting sustainability, social responsibility, and accountability, with a focus on alignment with the United Nations Sustainable Development Goals (SDGs). As recommender systems become increasingly integrated into daily interactions, they must go beyond personalization to support responsible consumption, reduce environmental impact, and foster social good. We explore strategies to mitigate the carbon footprint of recommendation models, ensure fairness, and implement accountability mechanisms. By adopting these approaches, recommender systems can contribute to sustainable and socially beneficial outcomes, aligning technological advancements with the SDGs focused on environmental sustainability and social well-being.

Problem

• Sustainable Recommendation Systems
• Energy Efficiency
• User Equity

Innovation

• Sustainable Recommendations
• Energy Efficiency
• Social Equity