Digital technologies and inclusion in humanitarian response - Digital_inclusion_synthesis.pdf

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• enial of access to needed resources, poor understanding of the particular needs of specific people or the personal biases of aid providers.

• an function as a first step towards improved service provision.

• n.

• Predictive forecast-based financing models use machine learning (ML) to trigger the release of funds to communities likely to suffer drought and food insecurity

• ensuring programmes provide assistance and services, including information and protection, to the most marginalised people in a humanitarian crisis

• ensure not just equal rights but also participation in humanitarian action

• digital tools offer the means to effectively scale up responses through better analytics, targeting and efficiency.

• powerful aid actors understand crisis-affected people

• While some people in crises use social media, digital mapping and other tools to self-organise and advocate for their rights, far more are recorded passively – whether they wish to be or not – by technologies such as biometric identification and satellite imaging

• refugees’ biometric data being passed without consent to the government of Myanmar

• hile they may be the sources of data about themselves and their communities, affected people often exercise little control over how that data is used

• impartiality

• equitable acces

• specific and diverse needs

• participation

• rights-based approac

• root causes of crises

• occur for both deliberate and unintended reason

• ns

• Digital identity systems