Machine learning and phone data can improve targeting of humanitarian aid

  • March 2022
  • Media Article
  • Publisher : Nature
Add to favourites
Add to favourites
My favourites
(Manage fav)
18567
(View all)
This article highlights how machine learning, combined with mobile phone data, can significantly enhance the targeting and delivery of humanitarian aid. By analyzing anonymized call detail records and other phone usage patterns, researchers can identify vulnerable populations and assess their needs more accurately and efficiently. This approach allows aid organizations to allocate resources more effectively, ensuring that assistance reaches those who need it most in times of crisis.

Author(s) :

Emily Aiken, et al

Yes

Get in touch with authors

No ratings yet

Rate this article

Yes

Key topics

Social Justice, Philanthropy and Human Rights

Also found in

Share

Join Our Newsletter

Explore More Articles

Book
  • March 2026

AI Systems for Social Good Playbook

This playbook serves as your step-by-step guide for designing responsible AI and data analytics solutions in the social sector. It guides both technical and non-technical teams through real-world projects, from understanding your context to monitoring your model post-deployment. Rooted in field-tested work, it aims to make AI more accessible, ethical, and impactful.
Case Study
  • February 2026

Powering Asia’s Philanthropy Boom: Why Investing in Talent and Capacity Matters Now

In this blog written for WINGS, ISDM — a WINGS member and contributor to the APAC working group — makes the case that Asia’s philanthropy boom can only deliver lasting impact if investment in talent and institutional capacity keeps pace with growing capital. Drawing on India’s experience, it highlights gaps in governance, compensation, and leadership pipelines, and calls on donors and ecosystem enablers to treat capacity-building as a core strategy, not an overhead.
Blog
  • March 2026

Code4Change a great platform to learn, collaborate, and understand how technical solutions can contribute to public systems like justice delivery

Participants from the third edition of Code4Change reflect on working with real-world justice system challenges and what it took to build meaningful solutions The Final Showcase of the third edition of Code4Change was held on 16 January 2026 at the International Centre Goa (ICG). The hackathon, organised by the Centre for Data Science and Social Impact (CDSSI) at ISDM, in partnership with DAKSH and the Centre for Social Sensitivity and Action (CSSA), Goa Institute of Management, centred on a single…
Case Study

The AI Platform Ending Pay Uncertainty in the Informal Sector

Kanak AI uses machine learning on real-world training data to predict income potential and recommend targeted upskilling courses, empowering job seekers to negotiate fair wages and build stable careers. Azad Ahmad (25), works as a Data Entry Operator in Gurgaon. He earns enough to support his family and sponsor his parent’s healthcare needs.. But it wasn’t always so. In 2021, when he graduated with a Bachelor’s degree, the COVID-19 pandemic was still raging, and he found himself navigating a tenuous…
We use essential and analytics cookies to operate this website and understand how visitors interact with it. As this site also functions as a login identity provider (IDP) for other ISDM portals, some cookies are necessary to enable secure authentication. By continuing to use this site, you consent to our use of cookies.
Accept & Continue