## 5 The Impact of AI on UWC: Sociopolitical Level
### Water, AI, and Gender  
Power imbalances and exploitation are key preoccupations of political ecologists; it is therefore no surprise that gender has been intensively studied, particularly in relation to water supply in developing countries [45; 46; 47]. Women are predominantly in charge of providing water for their families, often over very long distances, a fundamental task which does not enjoy social recognition. The responsibility of providing water intensifies the daily work of women and compromises their education and access to leisure, negatively impacting their health and quality of life [48]. These conditions may worsen in a climate change context, which access to water even more difficult [49].  
Gender inequality in the water sector is not restricted to developing countries; globally, the water sector continues to employ far less women than men, especially in technical and managerial jobs. A World Bank study shows that women occupy less than a quarter of the total water professionals. Moreover, even if on average, 23 percent of engineers and managers in a utility are female, 32 percent of the sampled utilities had no female engineers and 12 percent had no female managers [50]. As AI penetrates more and more aspects of the water management sector, this will have an impact on how women will cope with digitalisation and will make them more likely to be affected by redundancy. Thus, as AI applications become an essential interface in the work environment, the pressure to reskill will be harder on women; this learning process usually takes place during 'leisure' time, when women carry heavier care burdens than. This is correlated with the gender dimension of the 'digital gap', meaning that girls and young women do not have equal access to technology and digital training, which negatively affects literacy and professional prospects [51].  
Another area where the interplay between AI, water, and gender is worth considering is in the decision making in urban water planning interventions. As noted in Section 4.2, AI-based systems using aggregated user data (or instance, about water use in the household through smart meters) are increasingly employed to envisage scenarios and legitimise policies in urban water projects [40; 52]. If datasets are not disaggregated by sex and gender (as well as other identities), this means that policies regarding tariffs, water-saving schemes, or infrastructure programs will not factor in women's needs and opinions. This is especially important if we consider that care-related activities, which involve water use, are predominantly the responsibility of women. Using a gendered analysis, planners could gain a more accurate picture of communities, natural resource uses, households, and water users. Understanding the differences among and between women and men (who does what work, who makes which decisions, who uses water for what purpose, who controls which resources, who is responsible for different family obligations, etc.) can yield more effective results [53; 54]. Moreover, the fact that only 22 percent of AI professionals are women certainly does not improve the chances that the interpretation of these data includes a gendered perspective [55].  
It follows that woman should play a fundamental role in resource management and in the implementation of water technology and infrastructure projects. However, change is not at hand, with studies concluding that the interest of women in advancing within water governance is not well-supported by society, particularly in rural and peri-urban areas [46]. Governments and companies should strengthen women's participation and leadership, promote communication and education with a gender perspective, and ensure the collection of gender-disaggregated data.