Prize
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Kluz Prize for PeaceTech
Commit Global
Humanitarian crises are by definition global in nature, transcending national or regional borders. War is the most painful of human experiences and we need to ensure that victims and refugees find shelter and are protected in a timely manner. Commit Global built, deployed and is maintaining a first of its kind Humanitarian Digital Civic Infrastructure in support of the Ukrainian refugees. From ensuring their access to timely and accurate information, housing, transport and health support, to equipping UN and government agencies with the aid management tools they need for rapid effective intervention, an integrated ecosystem of interconnected digital solutions is making sure no refugee is left behind.
The ecosystem has assisted over 1.6 million refugees with verified trusted information on border crossing, legal information, access to social, medical, educational services, to safe verified housing and counseling in a continuous effort to combat human trafficking and other vulnerabilities. It was designed and implemented in under 48h since the start of the war and deployed in Romania in cooperation with the Romanian Government, UN agencies and national NGOs. Dopomoha.ro, available in 4 languages, is a single point of entry to the ecosystem where refugees can access help. The backend consists of complex case, stock, housing and services management apps that allow for effective response in the field. The initiative is the first integrated humanitarian assistance digital ecosystem bringing together on a shared infrastructure all the key actors involved in crisis response for an effective intervention. In 2022, the initiative was awarded as one of the ten projects of the year at the Paris Peace Forum. The ecosystem continues to support thousands of people every single day.
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Non Profit Organization Advancing Peace
Human Rights Data Analysis Group
The Human Rights Data Analysis Group (HRDAG) is a non-profit organization that applies rigorous science to the analysis of human rights violations. HRDAG collaborated with the Colombian Truth Commission (CEV in Spanish) and the Special Jurisdiction for Peace (JEP in Spanish) between 2020 and 2022 in the largest human rights data science project to date. The goal was to produce official statistical information about patterns of violence during the Colombian armed conflict. We used data from 112 datasets collected by 44 state institutions, victims’ organizations, and civil society organizations to analyze homicides, kidnappings, enforced disappearances, recruitment of child soldiers, and forced displacement. The results were included in the CEV’s Final Report and continue being used by the JEP.
Even with access to multiple databases, some human rights violations are never documented. Therefore, missing data is a central challenge, and our understanding of violence can be biased. Statistical methods can help overcome missing data to confront the truth.
Our analyses consisted of three main components. First, we used semi-supervised machine learning to link and deduplicate the records. Second, we used statistical imputation to probabilistically fill in missing information in observed records. Here we used neural networks to include relevant information from unstructured text fields. Third, we used multiple systems estimation to estimate the universe of victims, including the underreporting. We leveraged cloud computing solutions to run estimates for over 350,000 strata.
In 2023, we published analysis-ready data along with the R package ‘verdata’ to help researchers and practitioners replicate the results and answer new questions about patterns of violence in Colombia.
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Big Tech Building Peace
Palantir Foundry
An estimated 12 million people have fled their homes since Russia’s brutal invasion of Ukraine began, according to the United Nations — resulting in one of the largest humanitarian catastrophes since the Second World War.
With countries in Europe and beyond stepping up to come to the assistance of Ukraine and those displaced, the UK government launched the Homes for Ukraine (HFU) scheme, built on Palantir’s Foundry software. This is a novel decentralised immigration scheme with the aim of quickly providing safe passage and resettlement to refugees.
In just over a year, it has helped ensure the safe matching and resettlement of over 130,000 Ukraine refugees in the UK to date. Palantir’s Foundry software is the operational platform for the scheme. Through unprecedented cross-governmental collaboration enabled by the software, more than 3,000 users across local and central government use the platform to manage the operations of the resettlement scheme end-to-end, drastically speeding up the time taken to get refugees safely accommodated and resettled into the UK.
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Innovative Use of AI/ML
Project Didi
Project Didi uses artificial intelligence, machine learning (NLP, LLM) and big data to transform Zartman's Ripeness Theory from a qualitative academic concept into a quantitative tool for use in real-time. This is step one in a larger endeavor to transform qualitative concepts and theories from the field of peacebuilding and mediation that are firmly backed by academic research into quantitative data-driven tools to help solve entrenched conflicts in real-time. To date we have developed a model for identifying ripeness that includes the parameters and conditions held in consensus by academia. We tested our model on the Troubles in Northern Ireland. During our first phase we analyzed over 200 events (terrorism, political acts, rallies, police violence, diplomacy, etc.) in the decade leading to the ratification of the Good Friday Agreement.
Our algorithm successfully identified moments of ripeness in alignment with academic literature. We were also able to track levels of hurt (MHS) and way-out by side (Catholic, Protestant, British), which are the core pillars of Ripeness Theory. In our next phase we performed a discourse analysis using machine learning (ChatGPT 3.5 using single-shot classification) on 10,000 speeches made in the UK Parliament that related to the conflict itself. Our output matched that of our previous stage, also in accordance with the academic corpus. In other words, we were able to identify moments of ripeness over a decade by analyzing only political statements. In our next phase we will apply our model on an ongoing conflict (Palestinian-Israeli) and perform discourse analysis powered by machine learning algorithms on the 24/7 news cycle, social media data, socioeconomic factors and more to identify the parameters and conditions of Ripeness.