## 6 Discussion: A Hydrosocial Approach to Digital Water  
This paper aimed to move beyond the overly optimistic approaches to 'digital water' management and paint a more nuanced picture of this crucial transformation of the sector. Using a hydrosocial and Urban Political Ecology theoretical lens (see also [56]), the analysis highlighted that the gradual penetration of Big Data and AI-related technologies in the water sector can yield opportunities but also have negative effects such as increased surveillance, lack of accountability, workforce exclusion, discrimination, injustice, and loss of democratic quality. These are also areas where more data and research is needed. How to tackle these concerns? The available ethical frameworks provide some valuable guidance. Thus, the High-Level Expert Group on AI have defined a set of requirements that AI practitioners need to put in place to guarantee a Trustworthy AI: human agency and oversight; technical robustness and safety; privacy and data governance; transparency; diversity, non-discrimination, and fairness; societal and environmental wellbeing; and accountability [36]. However, these principles require further operationalisation for specific AI-based systems in the water domain. From the overview of opportunities and concerns presented in this paper emerges that a _hydrosocial approach to digital water_ is timely and necessary. It envisions AI as a force in the service of the human right to water, the implementation of which needs to be (1) _critical_, in that it takes into consideration gender, race, class, and other sources of discrimination and orients algorithms according to key principles and values; (2) _democratic and participatory_, i.e., it combines a concern for efficiency with sensitiveness to issues of fairness or justice and a shift towards mechanisms that ensure democratic control over the applications, while overcoming the split between experts and society; and (3) _interdisciplinary_, meaning that it integrates social sciences and 'technical'/ natural sciences from the outset, in all applications. Applying these principles to the risks identified here, the following recommendations emerge:  
1. Risk of surveillance: applications such as sewer epidemiology and smart metering have the capacity to obtain intimate private information which, if not properly protected, can become a tool of increased surveillance and oppression [57]. To this purpose, the direct and indirect uses of data coming from water must be limited, particularly when talking about aggregated data combined with nonwater information. In the EU, the TrustworthyAI ethical guidelines' [36] recommendations and AI Act provide a proper context to guarantee privacy, autonomy, and nondiscrimination; however, they only apply in the EU and they need further operationalisation for the water domain. 2. Risk of biased decision making: the advance of environmental decision-support systems, agent-based modelling, and Big-Data-powered scenarios for policymaking can be nonrepresentative, biased, and lack accountability. Therefore, there needs to be an increased preoccupation with standards of transparency and access to knowledge. Standards should be complemented by training programmes and integration of a wider variety of disciplines, knowledges, and perspectives in the design of the models, lowering the expertise and gender barrier. 3. Risk of workforce exclusion: digitalisation will inevitably impact the water workforce, provoking redundancy and the need for reskilling. To avoid the worst consequences for economic means, wellbeing, or dignity of this workforce, there needs to be increased awareness on the part of the policymakers and corporations; the advance of AI should be approached systemically, in an integrated way, and complemented with investments in training, while exploring substantial solidarity programmes such as a universal income. Specific attention needs to be paid to training for women in future professional roles, starting from acknowledgment of the gender dimension of the digital divide. 4.