When Early Warning Is Built on Uncertain Data
Reflections on Geospatial Artificial Intelligence for Atrocity Prevention
GeoAI is increasingly positioned as a transformative tool for humanitarian early warning and early action. GeoAI is the integration of geospatial data and artificial intelligence to analyze, model, and predict spatial patterns and processes. Geospatial data is used to train models to recognize patterns, classes, and features, enabling more effective spatial analysis and interpretation. From satellite imagery to incident reporting systems, spatial analytics promise to identify emerging risks before violence escalates. Yet, in practice, the effectiveness of these systems is constrained not by algorithms alone, but by the nature of the data and the ethical responsibilities attached to its use.
These reflections emerge from applied work developing early warning web maps and analytical tools for atrocity prevention in South Sudan, as a graduate student trainee at the Harvard Humanitarian Initiative’s Atrocity Prevention Lab.
The Reality of Humanitarian Data
Humanitarian datasets rarely arrive in an ideal form. Incident records are often compiled under conditions of insecurity, limited access, and uneven reporting capacity. In many cases, datasets lack accompanying documentation, contain ambiguous variable encodings, or rely on legacy data entry systems.
Variables such as gender or sex may be numerically coded without definition. Categories describing killings, kidnappings, sexual violence, or torture may be embedded within victim attributes rather than contextual dynamics. These are not merely technical inconveniences; they shape what GeoAI systems can learn and how risk is ultimately represented.
Before early warning can responsibly inform early action, foundational questions about data meaning and structure must be confronted.
Modeling Risk or Modeling Visibility?
One of the central challenges in atrocity prevention analytics is distinguishing between actual risk and the capacity to report violence. Areas with stronger reporting networks may appear more volatile, while remote or marginalized regions remain underrepresented.
When GeoAI models are trained on such data without correction, they risk predicting visibility rather than vulnerability. Early warning systems may then reinforce existing blind spots rather than illuminate emerging threats.
Addressing reporting bias is not optional; it is essential for ethical and effective atrocity prevention.
The Ethics of Prediction Under Uncertainty
In conflict settings, uncertainty is unavoidable. Ground truth may be delayed, incomplete, or politically sensitive. Yet early warning systems often translate probabilistic outputs into seemingly definitive risk maps.
This raises critical ethical questions. How should uncertainty be communicated to decision-makers? What level of confidence is sufficient to justify intervention—or restraint? And how do we prevent overconfidence in models when the consequences of error can be severe?
Responsible GeoAI must make uncertainty visible rather than obscure it.
From Early Warning to Early Action
Despite advances in spatial modeling, many humanitarian systems remain stuck in early warning mode. Risk is identified, mapped, and reported—but not always acted upon.
This gap is rarely technical alone. Institutional workflows, governance structures, and decision-making thresholds often determine whether early warning leads to preventive action. Without alignment between analytical outputs and operational realities, even the most sophisticated models risk irrelevance.
Early action requires not just better models, but better integration between analytics and humanitarian decision-making.
Who Defines the Variables That Matter?
Gender, sex, weapon type, and perpetrator categories are frequently treated as standard variables. Yet their definitions vary across contexts and datasets. When these inconsistencies are absorbed into GeoAI pipelines without scrutiny, they can introduce analytical noise or reinforce harmful assumptions.
Humanitarian GeoAI must remain attentive to how variables are defined, by whom, and for what purpose. Technical efficiency should not override contextual integrity.
Accountability in Humanitarian AI
As GeoAI increasingly informs atrocity prevention efforts, accountability becomes more complex. When a model fails to identify an emerging threat or falsely signals risk, responsibility is often diffused.
Clear governance frameworks are needed to define accountability across data collection, model development, interpretation, and decision-making. Without them, humanitarian AI risks becoming authoritative without being accountable.
Centering Human Judgment
GeoAI should not replace human expertise. Field analysts, local practitioners, and affected communities provide essential contextual knowledge that no model can fully capture.
The most effective early warning systems are those that amplify human judgment rather than displace it using GeoAI as a decision-support tool, not a decision-maker.
Conclusion
GeoAI holds significant promise for atrocity prevention and humanitarian early action. Yet its success depends less on computational power than on ethical clarity, data integrity, and institutional responsibility. Early warning is not simply a technical challenge. It is a humanitarian one.