AI and water systems: Do it right not just fast, says study

Can we prevent unforeseen consequences resulting from taking action in haste that could cripple future generations? CSER has ideas.
Amal Jos Chacko
AI and water, what is the link.jpg
AI and water, what is the link?

Mara Duchetti/iStock 

It is 2023. Self-driving cars are a reality, and so are 3D printing and artificial intelligence (AI). Man, however, is yet to provide the entire world access to clean water.

With AI-driven solutions becoming increasingly prevalent in various domains, it is natural that the idea has been floated to solve the water crisis humanity faces.

A team of researchers at the Centre for the Study of Existential Risk (CSER), an interdisciplinary Research Centre within the University of Cambridge, takes a cautionary note and analyzes this opportunity for potential problems.

While engineering developments have produced results and solved numerous problems, it can’t be denied that these have occasionally resulted in ‘progress traps’. Events such as Ancient Rome’s lead plumbing, initially heralded as a wonder, connecting its population with clean water and clearing wastewater away, were later found to have contaminated harbor water with lead, poisoning aquatic life.

CSER specializes in the study of risks associated with futuristic technological developments and is dedicated to understanding them, identifying such risks in advance, and taking the necessary steps to mitigate them.

AI and water systems: Do it right not just fast, says study
Example benefits of AI for solving problems across water systems.

The rewards that could be.

The team’s findings, published in the scientific peer-reviewed journal Nature, describe possible complications, including system-wide compromise stemming from design errors, malfunction, and cyberattacks as well as exposures to critical infrastructure failures exacerbating the problem at hand.

The much-talked-about three levels covering water systems are the water supply level, water distribution and disposal level, and water demand (end-user) level.

AI-driven solutions could help maintain water table limits and dam-filling schedules to reduce harm to aquatic ecosystems while monitoring reservoir inflows and dam telemetry to manage spillway releases.

At the network level, AI paired with sensors could speed up the development of new infrastructure and efficiently manage aging critical assets. Any reduction of network leakage, which currently is at 45 billion liters of potable water per day in developing countries, would drastically reduce our plight.

A combination of IoT devices— intelligent toilets, taps, and smart meters— and AI could create a similar impact at a community level. Unnecessary consumption in the agriculture sector and households could be cut.

Predictive demand and pricing analytics could be leveraged to drive further behavioral change toward conservation and reducing unnecessary consumption.

Reaping these rewards responsibly.

The team’s findings acknowledge the benefits AI could bring to the water sector and suggest three key recommendations to prevent any undesired consequences.

These measures range from the responsible deployment of AI across water supply systems to institutional, software, and hardware fail-safes to prevent bad actors from infiltrating and taking control.

Furthermore, they propose a six-layer systematic benefit and risk assessment framework that could help prioritize applications.

Study Abstract

Artificial intelligence (AI) is increasingly proposed to address deficiencies across water systems, which currently leave about 25% of the global population without clean water, about 50% without sanitation services and about 30% without hygiene facilities. AI is poised to enhance supply insights, catchment management and emergency response, improve treatment plant and distribution network design, operation and maintenance, and advance service availability, demand management and water justice. However, proliferation of this nascent technology could trigger serious and unexpected problems, including system-wide compromise owing to design errors, malfunction and cyberattacks as well as exposures to cascading socio-ecological, water–energy–food nexus and coupled critical infrastructure failures. In response, we make three recommendations for safe and responsible deployment of AI across potable water supply and sewage disposal systems: address gaps in foundational infrastructure and digital literacy; establish institutional, software and hardware mechanisms for trustworthy AI; and prioritize applications based on our proposed systematic benefit and risk assessment framework.

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