IIT Guwahati Professor Delivers Keynote Address at World Water Day Event Convened by IEI and APCB
- Prof Anamika Baruah
- Mar 29
- 4 min read
Updated: Apr 8
Published 23 March 2026 | Category Editorial and Opinion | Office Humanities and Social Sciences
Prof. Anamika Barua (Department of Humanities and Social Sciences & Centre for Sustainable Water Research, IITG) was invited as the keynote speaker to share her expertise on the structural links between gender equality (SDG 5) and water access (SDG 6).
Below, Prof. Barua expands on her keynote message, arguing that gender equality is not merely a benefit of better water systems, but a requirement for their success.
The Design Flaw: Why Gender is an "Upstream" Input for Water Security
World Water Day 2026 links two global goals that are often treated separately: access to water and sanitation, and gender equality. In practice, the failure of one is frequently a consequence of the failure of the other
What happens when water and sanitation systems are designed without women?
In many informal urban settlements, water arrives unpredictably, often for only a few hours a day. Someone has to wait. In the majority of households without on-premises taps, that responsibility falls to women and girls. They collect, store, and manage water because the system demands it. The cost is measured in time, safety, and—all too often—education.
Now consider a school sanitation block. It is built, funded, and counted toward coverage targets, yet usage remains low among girls. The lighting is poor, there is no provision for menstrual waste, and maintenance is irregular. The system works on paper; in practice, it fails.
These are not isolated failures. They are examples of embedded inefficiency—systems that underperform because they are designed with incomplete information.
That incompleteness has a pattern. The people who manage water daily—who know when supply arrives, which routes are safe, how long queues last, and what makes a facility usable—are often not present when infrastructure decisions are made. As a result, technically sound systems are placed in the wrong locations, designed without critical features, or scheduled in ways that do not align with actual use.
The consequence is not only inequity. It is inefficiency: underused assets, higher maintenance burdens, and systems that require workarounds from the very people they are meant to serve.
Why does gender equality improve water system performance?
The connection between gender equality (SDG 5) and water access (SDG 6) is not incidental; it is structural. The issue extends beyond field-level planning into institutions. In India, women account for a substantial share of science and technology graduates, yet their representation declines at higher levels of technical employment and decision-making.
This “leaky pipeline” does not simply affect representation. It shapes what kinds of questions are asked, what risks are prioritised, and what data is considered relevant.
The women being filtered out of technical water sector roles and the women collecting water at 5:00 AM need not be the same demographic. The leaky pipeline need not cost a direct transfer of lived water management experience into institutional spaces.
What it costs is something more diffuse, and equally consequential: the presence of women who understand—through proximity, family, and community—what it means to be a water manager. Women are more likely to ask for safety audits of infrastructure placement, more likely to insist on time-burden assessments in demand modelling, and more likely to notice that an aggregate usage figure conceals the fact that a system is not working for half its users.
The result is not that male-led teams cannot design effective systems. It is that teams without diverse perspectives are less likely to identify what they do not know—and less likely to seek out missing information. In water systems, those blind spots often map directly onto gendered patterns of use.
How can AI and data affect gender bias in water systems?
These gaps are increasingly being formalised in digital systems. AI-driven tools trained on historical data can optimise decisions efficiently, but only within the limits of the data they are given. If that data does not capture how systems perform differently for women and men, the outputs will not either.
To understand why, it helps to understand what gender-disaggregated data is and what its absence costs. Gender-disaggregated data simply means data that is recorded and analysed separately for women and men, rather than as an undifferentiated whole. Without it, systems can appear to perform well on average while failing significant segments of their users.
For instance, a public toilet could see high overall use but be completely avoided by the women of the community. AI-driven planning tools will optimise for a population that does not include the people who depend on the system most. The more capable the AI, the more efficiently it encodes the original knowledge deficit into the next generation of infrastructure and policy.
The algorithm does not introduce bias; it reproduces it at scale.
What does better water governance actually look like?
There is clear evidence that this gap can be addressed. In rural water governance initiatives in India, including those supported under the Jal Jeevan Mission, increased participation of women in local water and sanitation committees has influenced decisions on infrastructure placement, safety, and maintenance. In several cases, this has led to systems that are more consistently used, better maintained, and more equitably distributed.
For example, women members can object to the proposed location of a new water point. A site that is central, easy to connect to the supply line, and technically efficient can appear ideal on a map. However, the women know that the location requires passing through an area that is poorly lit and isolated in the early morning, when most collection happens. They can propose a different site—one that is slightly less efficient on paper, but actually used. What changes in these contexts is not the engineering itself, but the information available at the point of design.
Convergence between SDG 5 and SDG 6
In practice, SDG 5 and SDG 6 are not two goals running in parallel; they are one informing the other at the point where it matters most: the design stage. If water systems are to function as intended, they must be designed with the knowledge of the people who use and manage them most. Without that, inefficiency is built in from the start.
Gender equality is not a downstream outcome of improved water systems. It is an upstream input into their effectiveness.
Author Prof. Anamika Baruah
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