Issue #3: The data she's not in
Introduction
Welcome to issue 3! This issue lands around International Women's Day (March 8th 2026), and while that's not the reason for the topic, it's not a coincidence either. The question of how women experience disasters differently to men is not new. What is relatively new is what happens when that uneven experience gets baked into the datasets that AI systems learn from.
In the last issue, we asked: when you know an AI system's accuracy is unevenly distributed, what does responsible deployment actually look like? One place to start answering that is to ask where the unevenness comes from. What if it doesn't start with the system, but with the data?
Let’s remind ourselves of our four lenses from issue one:
Power and Agency
Data and Consent
Accountability and Governance
Operational Reality
Each issue we will be looking at a topic through one or more of these lenses.
Today's topic: If the data that shapes crisis decisions doesn't see women clearly, what happens to the help that follows?
Primary lens: Data & Consent | Secondary lens: Power & Agency.
Let's go….
Who disasters hit hardest
Let's start with what we know. A landmark study published in the Annals of the Association of American Geographers analysed disaster impacts from 141 countries over two decades. It found that natural disasters on average kill more women than men, and that the stronger the disaster, the wider the gap becomes. But here's the critical finding: the higher women's socioeconomic status in a given country, the weaker this effect. In other words, the vulnerability isn't biological. It is structural. It is built into the everyday patterns of who has access to information, resources, and decision-making power - which historically is usually men.
This plays out in specific ways. During Bangladesh's 1991 cyclone, women were three to five times more likely to die than men. Research attributed this primarily to women's limited access to warning information and their lack of agency in deciding how to respond to the hazard. Evacuation decisions often depended on male household members, reflecting social norms that limited women’s independent mobility and access to warnings.
Now, before we go further, this is not a simple story of "women always lose", nor is it diminishing the challenges unique to men that arise during a disaster response. As always, context matters enormously. Men account for the majority of flood-related deaths in Europe and the United States (often around two-thirds), largely linked to risk-taking behaviour and rescue activity. So you see, the gendered impact of disasters is not uniform, it depends on the social, cultural and economic context in which the disaster occurs. However, globally, the pattern is clear: existing inequalities are amplified, not equalised, by crisis.
Falling into the gap
So we know that men and women experience disasters differently and you could reasonably expect that the data we collect during and after disasters would reflect this. However, yes you guessed it…. it often doesn't.
A World Bank report on gender and disaster risk found that disaster risk management lags behind other sectors in the collection and reporting of sex and age disaggregated data. Reviews of disaster risk management under the Hyogo Framework found that sex- and age-disaggregated data was rarely collected or analysed, and gender considerations were often absent from post-disaster needs assessments. In addition, many countries still do not report disaggregated data even on the most basic indicators: deaths, injuries, and direct losses.
This matters for a simple reason. If you don't count it, you can't see it. And if you can't see it, you can't act on it.
There are also subtler ways the data gets skewed. The same report found that information on affected populations is often limited to aggregated numbers at the household level, rarely capturing individual-level data on damages and losses. When women lack access to bank accounts and hold a larger share of their assets in tangible form (eg, livestock, jewellery, stored crops etc), those assets are both more vulnerable to disaster and less visible in standard loss assessments. A destroyed house may get counted, but informal or unregistered assets will not. The report explicitly called for collecting more information on damages and losses at the individual rather than household level. The result is that data collection practices can systematically make women's experiences less visible, not because anyone set out to exclude them, but because the default methods weren't designed to include them.
And it goes deeper still. A study of the Yemen humanitarian response, published in the International Journal of Information Management, conducted 25 interviews with humanitarian managers and analysts and reviewed 47 reports and datasets. The researchers found evidence of a cycle of bias reinforcement, in which biased data at the field level cascaded upward through headquarters and donor decision making levels of the response. Among the four types of bias they identified, sampling bias was particularly revealing: respondent groups were frequently gender-imbalanced, with males overrepresented. In some cases, questions on sexual and gender-based violence were removed from surveys in order to obtain approval from local authorities to conduct them. So the very harms that disproportionately affect women were being edited out of the data before it was even collected. Although Yemen is a conflict rather than a natural disaster context, the dynamics of bias reinforcement in data collection apply across crisis types.
What this means for AI
If you read Issue 2, you'll recognise the shape of this problem. Last time, we looked at how the vantage point of a damage assessment system could create blind spots, with satellite imagery systematically under-reporting damage that was visible at closer range. The issue wasn't the AI model itself, it was the data the model learned from.
This issue extends that logic, in that if disaster data systematically underrepresents women's experiences, then any AI system trained on that data will inherit the gap. It will learn that the patterns in the data are the patterns that matter.
Think about what an AI system trained on this kind of data would absorb. It would learn that the household is the relevant unit for needs assessment. It would learn damage patterns from loss data that captures property and infrastructure but not the assets women are more likely to hold. It would learn to prioritise the types of harm that are most visible in existing datasets, which are the types of harm that existing collection methods were designed to capture, which are not the same as the types of harm that disproportionately affect women.
A scoping review in BMC Medical Ethics examining dozens of studies on AI in humanitarian crises found that biased data processing leading to inequitable assistance distribution was the most frequently cited ethical concern. The problem is not theoretical and as UNDP has noted, AI models trained on publicly available content tend to reflect the structural inequalities of the societies that produced the data, and these patterns are often reproduced or even amplified as models generate new outputs.
The quiet compounding
Let's pause on something. None of this requires malice and none of it even requires negligence in any individual decision. A needs assessment team interviews the people who are available. Asset registrations follow existing legal and property norms. Survey instruments are adapted to what authorities will permit. Data gets aggregated at the household level because that's the standard unit. Each of these is a practical, defensible choice. However, the combination produces a cumulative effect: a systematic underrepresentation of women's disaster experiences in the datasets that increasingly drive decisions, and when those datasets feed AI systems, the underrepresentation doesn't just persist - it scales.
This is what makes data bias different from data absence. Absence is visible: you can see a blank column, whereas bias looks like complete data. The dataset has entries, the model produces outputs, the dashboard displays results and everything appears to be working. Therefore, the gap is not in what the system shows, it is in what the system was never given to learn from.
Through the lenses
Through the Data & Consent lens, the issue is foundational. Data collection in crisis settings is already ethically fraught: consent is constrained, people have limited ability to refuse or control how their information is used, and the power imbalance between data collectors and affected populations is significant. When collection methods are also structurally gendered, failing to capture women's experiences or actively excluding certain types of harm from surveys, the data does not just reflect reality unevenly, it constructs a version of reality in which women's needs are systematically smaller than they actually are.
Through the Power & Agency lens, that constructed reality then shapes who gets what. If AI-driven resource allocation models, risk scores, or needs assessments are trained on gender-skewed data, they will produce outputs that appear objective while encoding existing inequalities. The communities and individuals whose experiences were underrepresented in the data will be underrepresented in the response. Not because someone decided they mattered less, but because the system's understanding of the situation was shaped by data that didn't fully see them.
So what do we do about this?
This is not an argument against data collection, or against AI in crisis response - both have value. However, there is a difference between collecting data and collecting data well, and there is a difference between a dataset that is large and a dataset that is representative.
The researchers who identified the bias reinforcement cycle in Yemen did not suggest abandoning data-driven response. They argued that practitioners and policymakers need to become aware of the biases in the data they use for decision-making, and that response organisations need to invest in identifying and mitigating those biases. The same applies to any AI system built on crisis data.
Sex and age disaggregated data is not a new concept. Frameworks exist. Standards exist. What often doesn't exist is the operational will, the funding, or the time to implement them properly under crisis conditions. That is an honest constraint, but it is also a choice about what we prioritise.
So it's over to you with this week's question:
If data collection practices in disasters have historically underrepresented women's experiences, and AI systems are now being trained on that data, who is responsible for the gap? And at what point does "we didn't have the data" stop being an acceptable answer?
See you in a fortnight. RB.
Sources & further reading
– Neumayer, E. & Plümper, T. (2007). "The Gendered Nature of Natural Disasters: The Impact of Catastrophic Events on the Gender Gap in Life Expectancy, 1981–2002." Annals of the Association of American Geographers, 97(3), 551–566. https://www.tandfonline.com/doi/full/10.1111/j.1467-8306.2007.00563.x
– Ikeda, K. (1995). "Gender Differences in Human Loss and Vulnerability in Natural Disasters: A Case Study from Bangladesh." Indian Journal of Gender Studies, 2(2), 171–193. https://journals.sagepub.com/doi/10.1177/097152159500200202
– Doocy, S., Daniels, A., Murray, S. & Kirsch, T.D. (2013). "The Human Impact of Floods: A Historical Review of Events 1980–2009 and Systematic Literature Review." PLOS Currents Disasters. https://pmc.ncbi.nlm.nih.gov/articles/PMC3644291/
– World Bank / GFDRR (2021). "Gender Dimensions of Disaster Risk and Resilience: Existing Evidence." https://www.worldbank.org/en/topic/disasterriskmanagement/publication/gender-dynamics-of-disaster-risk-and-resilience
– Paulus, D., de Vries, G., Janssen, M. & Van de Walle, B. (2023). "Reinforcing data bias in crisis information management: The case of the Yemen humanitarian response." International Journal of Information Management, 72, Article 102663. https://www.sciencedirect.com/science/article/pii/S0268401223000440
– Kreutzer, T., Orbinski, J., Appel, L., An, A., Marston, J., Boone, E. & Vinck, P. (2025). "Ethical implications related to processing of personal data and artificial intelligence in humanitarian crises: a scoping review." BMC Medical Ethics, 26, 49. https://pmc.ncbi.nlm.nih.gov/articles/PMC11998222/
– UNDP (2025). "AI, gender bias and development." https://www.undp.org/eurasia/blog/ai-gender-bias-and-development
