A Responsible Artificial Intelligence Framework for Groundwater Modeling

Chong Chen; Yulu Zhang; Qingxi Guo; Yihan Liu

cs.AIAug 16, 2026

A Responsible Artificial Intelligence Framework for Groundwater Modeling

Authors: Chong Chen, Yulu Zhang, Qingxi Guo, Yihan Liu

Organizations: College of Artificial Intelligence, China University of Petroleum – Beijing

Abstract

The rapid development and widespread application of artificial intelligence (AI) have sparked intense discussions on how to deploy responsible AI systems in a manner aligned with human values and ethical standards. Compared to fields like healthcare, energy, or finance, the application of AI in groundwater is relatively limited, and research on responsible AI is even more scarce. Taking the middle reaches of the Heihe River Basin as the study area, this paper proposes six Responsible AI principles: transparency, technical robustness, privacy governance, fairness, accountability, and sustainability. LSTM and Transformer time-series models are developed using multi-source hydrometeorological data, and validated via post-hoc interpretability, Monte Carlo simulation, and scenario analysis. The results show that Transformer outperforms LSTM in accuracy, robustness, and interpretability, demonstrating the operability and practical value of Responsible AI principles in groundwater prediction to support sustainable water management under climate change and human activities.

Responsible AI

Responsible AI studies how to design, evaluate, and govern AI systems to promote fairness, safety, transparency, accountability, privacy, and sustainability.

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Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI
Sep 25, 2026Farnaz Farid, Tashfia Towkee, Sania Nasreen +1Responsible AI AI Accountability
Western Sydney University · Southwest Jiaotong University
Context-Aware Concept Distillation for Trustworthy Flood Prediction
Jul 25, 2026Eli Levinkopf, Efrat Morin, Claudia V. GoldmanNeural Surrogate Modeling Explainable Artificial Intelligence
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel · Institute of Earth Sciences, The Hebrew University of Jerusalem, Israel · Hebrew University Business School, The Hebrew University of Jerusalem, Israel
Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction
Mar 26, 2026Matteo Salis, Gabriele Sartor, Rosa Meo +2Physics-Informed ML Attention Mechanisms
Computer Science Department, University of Turin, Turin, Italy · LISTIC Laboratory, Université Savoie Mont Blanc, Annecy-le-vieux, France · Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino and University of Turin, Turin, Italy

Sep 25, 2026J/K move · Enter open · S save

Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI

Farnaz Farid, Tashfia Towkee, Sania Nasreen, Sami bin Azad

Western Sydney University · Southwest Jiaotong University

Responsible AI AI Accountability

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As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption remains largely invisible. While energy and carbon impacts are widely recognized, the substantial freshwater demands of data center cooling and electricity generation receive little attention. To address this gap, we introduce SustainAI, a water-aware, closed-loop framework incorporating environmental accountability into AI deployment. SustainAI integrates real-time water metering, a hallucination-aware penalty model, and a water-aware routing algorithm that accounts for regional water stress. Evaluated via Small Language Models (SLMs) extracting health misinformation, results reveal an 11-fold variation in water footprint across geographically distributed data centers (0.0477 mL to 0.5360 mL per inference). Across 1,335 inference runs, the system consumed approximately 399 mL of water but produced only 240 correct outputs, demonstrating that substantial resources are spent on inaccurate responses. Crucially, SustainAI extends beyond technical optimization through a Care by Design lens, framing AI sustainability around relational ethics, regional equity, and ecological stewardship. By combining water monitoring, adaptive accountability, and Care by Design principles, SustainAI provides a practical foundation for integrating ethical care and environmental responsibility into AI infrastructure design and lifecycle management.

Abstract

Responsible AI

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