Tag Archives: aid

COVID-19 and Information Management in the Aid Sector

Guest post from Originally posted on Jan 20, 2021.

A few learnings from the COVID-19 crisis and its impacts on Information Management practices in the aid sector: improving information through inclusive processes and capacity building

 

Cette ressource est également disponible en français ici.

This resource was produced by CartONG and benefited from the methodological advice and in-depth review of Groupe URD, as part of its support to CartONG in the framework of the current project.

In the last year or so, the COVID-19 crisis has impacted the aid sector in multiple ways and on various topics. Humanitarian data management is no exception. In the past decade, information and data management has become more and more important in humanitarian responses. During this particular crisis, as conducting regular field data collection and exchanging with communities got more challenging, humanitarian actors were forced to adapt their data collection and information management practices. This very particular situation invites us to reflect on the difficulties faced by NGOs in Information Management (IM), the choices they made, how it fits into a larger reflection process in terms of discussing IM stakes and strengthening IM practices within the aid sector.

Since April 2020, CartONG has been implementing a project to support the humanitarian sector in adapting its Information Management and Monitoring & Evaluation response to the COVID-19 crisis. In the course of this initiative, our team has witnessed how operational NGOs adapted to the situation, as different phases of the crisis unfolded, as well as how their data and information management needs and practices evolved. We have also observed new trends in the sector (dashboarding, remote data collection) and reflected on the difficulties operational actors were facing in terms of data and information management.

Building on this enriching experience, this learning paper aims at providing an analysis of such evolutions, in particular looking at the impact of the crisis on internal information flows and responsibilities within aid NGOs and what the use of new IM tools meant in terms of IM practices. It also reflects on how these evolutions can help improve the quality of information produced by NGOs. CartONG’s perspective was complemented by the perspective of other H2H organizations and operational actors who have agreed to share their experience with us.

You can read the document by downloading it in PDF format by clicking here or by clicking on the image.

Whether you have found this resource useful or not, we would love to hear back from you 😊 Please take one minute to fill in this 5-question survey (https://framaforms.org/questionnaire-de-satisfaction-help-center-covid-19-1594987789) to help improve the guidance we will continue producing in the context of this project!

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This project was co-funded by the French Development Agency (AFD) and the H2H Network’s H2H Fund, the latter supported by UK aid from the UK government. Nevertheless, the ideas and opinions presented in this document do not necessarily represent those of the H2H Network, UK aid and AFD.

You can’t have Aid…without AI: How artificial intelligence may reshape M&E

by Jacob Korenblum, CEO of Souktel Digital Solutions

Photo: wikipedia.org/

Potential—And Risk

The rapid growth of Artificial Intelligence—computers behaving like humans, and performing tasks which people usually carry out—promises to transform everything from car travel to personal finance. But how will it affect the equally vital field of M&E? As evaluators, most of us hate paper-based data collection—and we know that automation can help us process data more efficiently. At the same time, we’re afraid to remove the human element from monitoring and evaluation: What if the machines screw up?

Over the past year, Souktel has worked on three areas of AI-related M&E, to determine where new technology can best support project appraisals. Here are our key takeaways on what works, what doesn’t, and what might be possible down the road.

Natural Language Processing

For anyone who’s sifted through thousands of Excel entries, natural language processing sounds like a silver bullet: This application of AI interprets text responses rapidly, often matching them against existing data sets to find trends. No need for humans to review each entry by hand! But currently, it has two main limitations: First, natural language processing works best for sentences with simple syntax. Throw in more complex phrases, or longer text strings, and the power of AI to grasp open-ended responses goes downhill. Second, natural language processing only works for a limited number of (mostly European) languages—at least for now. English and Spanish AI applications? Yes. Chichewa or Pashto M&E bots? Not yet. Given these constraints, we’ve found that AI apps are strongest at interpreting basic misspelled answer text during mobile data collection campaigns (in languages like English or French). They’re less good at categorizing open-ended responses by qualitative category (positive, negative, neutral). Yet despite these limitations, AI can still help evaluators save time.

Object Differentiation

AI does a decent job of telling objects apart; we’ve leveraged this to build mobile applications which track supply delivery more quickly & cheaply. If a field staff member submits a photo of syringes and a photo of bandages from their mobile, we don’t need a human to check “syringes” and “bandages” off a list of delivered items. The AI-based app will do that automatically—saving huge amounts of time and expense, especially during crisis events. Still, there are limitations here too: While AI apps can distinguish between a needle and a BandAid, they can’t yet tell us whether the needle is broken, or whether the BandAid is the exact same one we shipped. These constraints need to be considered carefully when using AI for inventory monitoring.

Comparative Facial Recognition

This may be the most exciting—and controversial—application of AI. The potential is huge: “Qualitative evaluation” takes on a whole new meaning when facial expressions can be captured by cameras on mobile devices. On a more basic level, we’ve been focusing on solutions for better attendance tracking: AI is fairly good at determining whether the people in a photo at Time A are the same people in a photo at Time B. Snap a group pic at the end of each community meeting or training, and you can track longitudinal participation automatically. Take a photo of a larger crowd, and you can rapidly estimate the number of attendees at an event.

However, AI applications in this field have been notoriously bad at recognizing diversity—possibly because they draw on databases of existing images, and most of those images contain…white men. New MIT research has suggested that “since a majority of the photos used to train [AI applications] contain few minorities, [they] often have trouble picking out those minority faces”. For the communities where many of us work (and come from), that’s a major problem.

Do’s and Don’ts

So, how should M&E experts navigate this imperfect world? Our work has yielded a few “quick wins”—areas where Artificial Intelligence can definitely make our lives easier: Tagging and sorting quantitative data (or basic open-ended text), simple differentiation between images and objects, and broad-based identification of people and groups. These applications, by themselves, can be game-changers for our work as evaluators—despite their drawbacks. And as AI keeps evolving, its relevance to M&E will likely grow as well. We may never reach the era of robot focus group facilitators—but if robo-assistants help us process our focus group data more quickly, we won’t be complaining.