Inteligencia artificial aplicada a la predicción de la pobreza

Horario: 10:00 hrs MX / 17:00 hrs UK

Descripción: On August 2, 2024, a law was passed in Mexico to dissolve the National Council for the Evaluation of Social Development Policy (Coneval), an institution responsible for measuring poverty and evaluating social programs. The disappearance of Coneval raises urgent concerns about the future of poverty measurement in Mexico, necessitating the development of new methodologies to monitor and address poverty effectively. Poverty remains a significant global challenge, with millions affected and efforts to reduce poverty at the forefront of the Sustainable Development Goals (SDGs), specifically SDG 1, which aims to eradicate poverty by 2030.The COVID-19 pandemic has exacerbated the struggle against poverty, reversing years of progress. In 2022, the World Bank estimated that 9.3% of the global population lived on less than $1.90 per day, highlighting the need for robust poverty monitoring tools. Telephone surveys and advancements in machine learning have become critical in this effort. For example, machine learning models, like those used in the World Bank’s SWIFT project, offer real-time poverty monitoring by integrating vast datasets, providing accurate and timely assessments even in crisis situations.

Ponente: Oscar Couoh Ramírez

Modalidad: En línea

Ubicación: Zoom y transmisión en vivo por Facebook y Youtube.

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