Integrating Values and Ethics when utilizing Artificial Intelligence and Machine Learning to support Water Management decisions

Data collection, analysis, and model development have been historically utilized to inform water management decisions, be it resource allocation, groundwater and surface water management, or addressing challenges such as water quality. Over time, AI/ML techniques have been developed and utilized to harness the abundance of data to support decision-making around water resource decisions. However, all decisions around water affect people and the environment. As the world of AI/ML progresses in the natural-human-social-environmental systems and we work towards equitable allocation of resources, questions arise around the ethics and values of how AI/ML is being applied to inform decisions. Are there inadvertent biases that could exist in the data that get carried through the AI/ML models? Could there be legacy effects of historical water management that, because of how the data was collected, generate alternatives which are not thoroughly explored for inherent biases? Then, decision-makers utilize those alternatives with little to no questions because they believe that AI/ML will give them the best answer? Are there opportunities to explore how and where biases could show up in the process, as well as methods developed in how values and ethics could be integrated into the decision-making to mitigate against this?

The goal of this Research Topic is to highlight advances in transdisciplinary and interdisciplinary research where human dimensions, social science, and AI/ML are successfully applied to address water management challenges. Revealing where working with communities, incorporating values, and utilizing advanced AI/ML techniques to develop alternatives which are thoroughly explored for biases such that alternatives reflect the values of the decision makers and communities affected by the decisions, as well as minimize potential inequities in the allocation of resources.

The scope of this Research Topic covers:

o Ethical considerations and values integration in AI/ML for sustainable water management.

o Collaborative approaches involving communities in AI-driven water resource decisions.

o Interdisciplinary and transdisciplinary research combining AI/ML with social sciences to address water-related challenges.

o Methodologies for uncovering and mitigating biases in AI models used for water management.

o Applications of decision-making frameworks like structured decision-making or decision making under deep uncertainty in water resource management.

Submissions are welcomed from transdisciplinary and multidisciplinary teams that intertwine AI/ML with social sciences or human dimensions to explore new methodologies and address ethical issues in water management. This includes research that investigates bias emergence in data and its translation through AI/ML models, contributing to more equitable and inclusive water resource practices.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Values, Ethics, Social Science, Human Dimensions, Water, Artificial Intelligence, Machine Learning, Structured Decision Making, Decision Making Under Deep Uncertainty, Surrogate Modeling

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.