A New Era of Responsible AI in the Water Sector


As per the latest figures released by UNICEF, one in three people in the world do not have accessibility to clean drinking water and at the same time, two out three people do not have accessibility to even a handwashing facility which is quite basic. It has been noted that the global population which is affected by the scarcity of water in form or another will reach up to 40%. Along with that, another huge problem that has become troublesome for many countries and cities around the world is the hygienic and safe management of sewage. 

 Types of AI-based water scarcity solutions

The AI-based remedies for water scarcity in the world can be in the following forms:-

Water usage optimization: a lot of complicated decision-making is involved in the usage of water for irrigation and industrial purposes. This can be resolved by using the appropriate and modern technologies in AI.

Distribution system supervision and maintenance: Monitoring pipes, nozzles, and outlets on a big scale is extremely difficult and requires a significant amount of dedicated human resources to be successful in executing it. An AI-based system, on the other hand, can troubleshoot many of these issues in real-time. On a broad scale, this can reduce huge quantities of water wastage.

Decreasing Uncertainty: AI can assist in forecasting uncertainties. For example, it can predict the disruption of water supply due to man-made or environmental hazards. At times of water scarcity, they can also aid in building ethical distribution models. 

Highlighting responsible AI

The potential for AI as a field to normalize the marginalization of various persons in the name of efficiency is an increasing concern among data scientists and philosophers of science. As AI becomes more widely employed in critical services such as water supply, the need to address these ethical concerns becomes more imperative.

Several issues in the water supply are dealt with in various ways within the water management systems based on AI. Firstly, one approach to do the same is to incorporate fair distribution principles into the algorithms of AI, such as equitable distribution or minimal water availability per home. Secondly, AI systems can recognize and generate indices of accessibility using multiple sources of data like census and GIS. This will take into consideration of a bigger set of variables in the case of decision making which goes beyond economic efficiency to be a socially responsible AI.  

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