Tag Archives: development

Three Tips for Bridging Tech Development and International Development 

by Stephanie Jamilla

The panel “Technology Adoption and Innovation in the Industry: How to Bridge the International Development Industry with Technology Solutions” proved to be an engaging conversation between four technology and international development practitioners. Admittedly, as someone who comes from more of a development background, some parts of this conversation were hard to follow. However, three takeaways stuck out to me after hearing the insights and experiences of Aasit Nanavati, a Director of DevResults, Joel Selanikio, CEO and Co-Founder of Magpi, Nancy Hawa, a Sofware Engineer from DevResults, and Mike Klein, a Director from IMC Worldwide and the panel moderator. 

“Innovation isn’t always creation.”

The fact that organizations often think about innovation and creation as synonymous actually creates barriers to entry for tech in the development market. When asked to speak about these barriers, all three panelists mentioned that clients oftentimes want highly customized tools when, they could achieve their goals with what already exists in the market. Nanavati (whose quote titles this section) followed his point about innovation not always requiring creation by asserting that innovation is sometimes just a matter of implementing existing tools really well. Hawa added to this idea by arguing that sometimes development practitioners and organizations should settle for something that’s close enough to what they want in order to save money and resources. When facing clients’ unrealistic expectations about original creation, consultancies should explain that the super-customized system the client asks for may actually be unusable because of the level of complexity this customization would introduce. While this may be hard to admit, communicating with candor is better than the alternative — selling a bad product for the sake of expanding business. 

An audience member asked how one could convince development clients to accept the non-customized software. In response, Hawa suggested that consultancies talk about software in a way that non-tech clients understand. Say something along the lines of, “Why recreate Microsoft Excel or Gmail?” Later in the discussion, Selanikio offered another viewpoint. He never tries to persuade clients to use Magpi. Rather, he does business with those who see the value of Magpi for their needs. This method may be effective in avoiding a tense situation between the service provider and client when the former is unable to meet the unrealistic demands of the latter.

We need to close the gap in understanding between the tech and development fields.

Although not explicitly stated, one main conclusion that can be drawn from the panel is that a critical barrier keeping technology from effectively penetrating development is miscommunication and misunderstanding between actors from the two fields. By learning how to communicate better about the technology’s true capacity, clients’ unrealistic expectations, and the failed initiatives that often result from the mismatch between the two, future failures-in-the-making can be mitigated. Interestingly, all three panelists are, in themselves, bridges between these two fields, as they were once development implementors before turning to the tech field. Nanavati and Selanikio used to work in the public health sphere in epidemiology, and Hawa was a special education teacher. Since the panelists were once in their clients’ positions, they better understand the problems their clients face and reflect this understanding in the useful tech they develop. Not all of us have expertise in both fields. However, we must strive to understand and accept the viewpoints of each other to effectively incorporate technology in development. 

Grant funding has its limitations.

This is not to say that you cannot produce good tech outputs with grant funding. However, using donations and grants to fund the research and development of your product may result in something that caters to the funders’ desires rather than the needs of the clients you aim to work with. Selanikio, while very grateful to the initial funders of Magpi, found that once the company began to grow, grants as a means of funding no longer worked for the direction that he wanted to go. As actors in the international development sphere, the majority of us are mission-driven, so when the funding streams hinder you from following that mission, then it may be worth considering other options. For Magpi, this involved having both a free and paid version of its platform. Oftentimes, clients transition from the free to paid version and are willing to pay the fee when Magpi proves to be the software that they need. Creative tech solutions require creative ways to fund them in order to keep their integrity.

Technology can greatly aid development practitioners to make a positive impact in the field. However, using it effectively requires that all those involved speak candidly about the capacity of the tech the practitioner wants to employ and set realistic expectations. Each panelist offered level-headed advice on how to navigate these relationships but remained optimistic about the role of tech in development. 

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.