The children’s names below appear on a list of victims of Israel’s offensive in Gaza, maintained by health authorities in the territory. As of the end of July it ran to 60,199 names, of whom 18,457 were under 18s. Far from comprehensive, the list does not include the thousands still buried under the rubble of destroyed buildings, as well as the war’s many indirect victims As of the end of July there were 18,457 children named on the long official list of Palestinian victims of Israel’s war in Gaza. Over almost two years, that is equivalent to bombs, bullets and shells killing a boy or girl every hour of every day. In reality the deaths are not spaced out so evenly. Siblings, cousins and playmates are often killed together, by an airstrike or artillery shell. Even children shot by snipers or quadcopters are sometimes brought into hospitals in groups, doctors say. Continue reading...

Advances in artificial intelligence offer an enticing solution to a global problem: perhaps interacting with large language models (LLMs) can help all…

Uddannelse | Politikens dækning af uddannelsesområdet med temaer som Studeliv og Højskoleliv samt klummer om livet som studerende, reformer og meget andet.

High-resolution human settlement maps provide detailed delineations of where people live and are vital for scientific and practical purposes, such as rapid disaster response, allocation of humanitarian resources, and international development. The increased availability of high-resolution satellite imagery, combined with powerful techniques from machine learning and artificial intelligence (AI), has spurred the creation of a wealth of settlement datasets. The agreement and alignment between these datasets has not been studied in detail. We compare three settlement maps developed by Google (Open Buildings), Meta (High Resolution Population Density Maps) and Microsoft (Global Building Footprints), and uncover which factors drive mismatch. Our study focuses on 44 African countries. We build a global machine learning model to predict where datasets agree, and find that geographic and socio-economic factors considerably impact overlap. However, we also find there is great variability across countries, suggesting complex interactions between country morphology and dataset overlap. It is vital to understand the shortcomings of AI-derived settlement layers as international organizations, governments, and NGOs are already experimenting with incorporating these into programmatic work. We anticipate our work to be a starting point for more critical and detailed analyses of AI derived datasets for humanitarian, policy, and scientific purposes.

High-resolution human settlement maps provide detailed delineations of where people live and are vital for scientific and practical purposes, such as rapid disaster response, allocation of humanitarian resources, and international development. The increased availability of high-resolution satellite imagery, combined with powerful techniques from machine learning and artificial intelligence (AI), has spurred the creation of a wealth of settlement datasets. The agreement and alignment between these datasets has not been studied in detail. We compare three settlement maps developed by Google (Open Buildings), Meta (High Resolution Population Density Maps) and Microsoft (Global Building Footprints), and uncover which factors drive mismatch. Our study focuses on 44 African countries. We build a global machine learning model to predict where datasets agree, and find that geographic and socio-economic factors considerably impact overlap. However, we also find there is great variability across countries, suggesting complex interactions between country morphology and dataset overlap. It is vital to understand the shortcomings of AI-derived settlement layers as international organizations, governments, and NGOs are already experimenting with incorporating these into programmatic work. We anticipate our work to be a starting point for more critical and detailed analyses of AI derived datasets for humanitarian, policy, and scientific purposes.