Claim-to-source matrix

Institutions use data, machine learning, simulation, or forecasting to predict collective behavior and guide interventions affecting groups or populations.

Forecasting of protests, conflict, migration, crime, public health, markets, voting, compliance, or other collective behavior to guide intervention.

Ordinary descriptive statistics that do not predict or direct intervention, and aggregate planning systems that cannot be used to identify individuals may present a substantially different risk.

The same architecture can support aid planning or preemptive repression depending on the unit of analysis, data, intervention, and due-process safeguards.

Forecasts can become self-fulfilling when intervention creates the data that validates the model.

High-risk labels can shift the presumption from individualized evidence toward statistical suspicion.

Combines large behavioral, administrative, location, and event datasets.

Produces granular risk scores and dashboards.

Simulates possible interventions.

Updates forecasts continuously.

Makes population-scale surveillance operationally useful.

Aggregate forecasting documented: Systems such as ViEWS and Project Jetson forecast conflict or displacement for planning and humanitarian response.

Individualized risk systems documented: Predictive policing and surveillance systems have scored people and places for intervention.

Accuracy strongly context dependent: Existing conflict continuation can be more predictable than rare novel events; local individual prediction often has poor validity.

Event and trend forecasting.

Network and mobility analysis.

Risk scoring of people or places.

Agent-based or scenario simulation.

Intervention feedback into future training data.

ViEWS political-violence early warning Documented academic system An open research project forecasts several forms of political violence at country and grid-cell levels. Methods and out-of-sample evaluations are publicly described. The system performs better for continuation or escalation of existing conflict than novel onset. Operational decisions and social response can change the events being forecast.

Chicago predictive policing and risk lists Documented public-sector deployment Police used person- and place-based risk systems to guide intervention. Audits found weak public-safety benefits and increased scrutiny or arrests for targeted people and places. Official reviews documented circular enforcement and limited effectiveness. Proprietary design and changing police practices make broader generalization difficult.

Historical enforcement bias becomes future prediction.

Risk scores can justify collective punishment or preemptive detention.

Predictions can create self-fulfilling feedback loops.

Data collection erodes privacy and freedom of assembly.

Rare events have low base rates and many false alarms.

Data gaps and censorship make some populations invisible.

Interventions change the outcome used to evaluate the model.

Point estimates conceal uncertainty and model disagreement.

Indicator: A person or neighborhood is repeatedly targeted because prior enforcement generated more data there. Caveat: Auditing requires comparing enforcement data with independent measures of underlying events.

Indicator: Decision-makers receive a single score without uncertainty or explanation. Caveat: A simple interface is not itself proof of an invalid model, but it increases misuse risk.

Indicator: Privacy tools or lawful association are treated as risk indicators. Caveat: Such proxies require strong legal scrutiny and contextual review.

Prefer aggregate supportive planning over individual coercive targeting.

Use out-of-sample validation, calibration, and base-rate-aware metrics.

Publish uncertainty and model limitations.

Require due process, notice, access, and human appeal.

Use data minimization and differential privacy.

Audit intervention feedback and disparate impact.

Evaluation when successful prevention makes a forecast appear false.

Governance of dual-use humanitarian mobility data.

Standards for model uncertainty in high-stakes decisions.

Long-term social effects of living under predictive surveillance.