Harnessing the Power of AI to Transform Participant Feedback in USAID Funding

By Hisham Jabi

#mena #usaid #worldbank #whitehouse #middleeast #AI

Introduction

In the constantly evolving landscape of development, one truth remains constant: the importance of participant feedback. The breakthrough initiative by USAID, titled "Collecting Feedback from Development Program Participants," marks a monumental stride towards linking policy aspirations with ultimate activity participant inputs. However, there's an elephant in the room—time. The traditional development cycle is slow, often taking 18 to 24 months, leaving a considerable gap in immediate and relevant participant feedback. So, where does Artificial Intelligence (AI) fit into this equation?

The Challenge of The Traditional Feedback Approach According to the USAID Project Cycle

While international development beltway policymakers have ambitious goals to make a difference and serve the poor, the process to realize these aspirations is lengthy. From initial assessments and budget approvals to resource mobilization and data collection and analysis, the time lag is substantial. Most striking is the time that elapses before the implementing partners and INGOs start receiving feedback from the participant communities. This feedback is crucial for gauging the relevance, quality, satisfaction, and unintended consequences of the aid provided. While the new guide from USAID has made strides in focusing on these elements, the methods to collect this feedback are still far from efficient.

Stages of Designing and Implementing a USAID-Funded Activity: A Closer Look

To fully understand the potential impact of AI in optimizing participant feedback, it's essential to examine the various stages involved in bringing a USAID-funded activity to participants:

Stage 1: Initial Assessment The process kicks off with an initial assessment to identify the needs, challenges, and goals for the country and the region, and include them in the State/USAID strategic objectives.

Stage 2: Budget Approval After the assessment, a budget is allocated for the regions, missions, and bureaus.

Stage 3: Develop Country Development Coordination Strategy with Detailed Performance Monitoring Plan To identify development objectives, Intermediate Results (IRs), and sub-IRs.

Stage 4: RFP/RFA Issuance A Request for Proposals or Applications (RFP/RFA) is then issued. Organizations submit their proposals.

Stage 5: Award Issuance Contracts are signed with the selected implementing partners (IPs) or INGOs, marking the official start of the activity's implementation phase.

Stage 6: Activity Kickoff The activity formally begins, with a focus on resource mobilization.

Stage 7: Data Collection Data starts to be collected for analysis, but this data often lacks real-time participant feedback.

Stage 8: First Feedback Loop The first cycle of participant feedback is usually collected at a late stage, if it is even considered in the award. Depending on the project's complexity, this could be months or even years after the initial assessment.

Stage 9: Activity Adjustment Based on the feedback and initial outcomes, activity plans could be adjusted. However, given the time that has already elapsed, these adjustments can often be too little, too late.

Stage 10: Ongoing Feedback and Adjustments in an ideal world, feedback loops would be continuous from this point onward, but traditional methods make this challenging.

?AI: The Game-Changer

?Artificial Intelligence can transform the feedback loop in unprecedented ways. Imagine a system capable of real-time analysis of vast datasets, instantly flagging issues that require immediate attention. Not only does this dramatically speed up the process, but it also allows for a more nuanced understanding of community perceptions and expectations. What's more, AI analytics can classify feedback, breaking it down by various demographic factors and participant cohort groups. This level of targeted analysis enables more individualized interventions, ensuring that the aid provided is not just a one-size-fits-all solution. Even more impressively, machine learning algorithms can discover complex patterns and trends in feedback data, uncovering insights that would likely be missed by traditional MEL methods. The capability to dig deeper into the feedback allows for more actionable strategies, leading to impactful social changes on the ground.

How Supervised AI and Deep Learning Work?

?Deep learning, a subset of machine learning, employs neural networks with large internal mathematical functional layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing them to "learn" from large amounts of data. One type of deep learning is Supervised AI, which works by learning from labeled data and making predictions or decisions based on it. When we collect digital feedback from participants through different channels—such as surveys, key informant interviews (KIIs) texts, and social media inquiries—we generate massive digital datasets. These datasets serve as input for supervised AI algorithms. The trained AI model processes this data through various layers of its neural network to provide desired outputs based on the input labels provided: Was the aid relevant? Was it satisfactory? Was it delivered in a timely manner? Was it of high quality?

Real-Time Adjustments

What makes supervised AI particularly compelling is its ability to continuously improve. As it processes more data, the system self-adjusts using algorithms, refining its predictions and ensuring that resource allocation is as effective as possible: the target of the feedback process. This is where deep learning comes in. Its layered neural networks can automatically adjust the 'weights' given to various factors in real-time, making the entire feedback loop more dynamic and responsive.

The Power of Data

Not too long ago, we didn't have enough data to train these sophisticated algorithms effectively. Now, we have the opposite problem: a deluge of data over the lifespan of a project, which could be five years or even more. This data-rich environment is precisely what makes deep learning algorithms thrive. The more participant feedback we can feed into the system, the better and more accurate the outcomes.

The Takeaway

Imagine a scenario where feedback isn't just a one-off exercise but an ongoing dialogue between participants, implementing partners and donors. Picture a world where aid organizations can immediately spot issues and rectify them, ensuring the most effective use of resources. This is no longer the stuff of imagination, but a reality made possible by the power of AI, particularly supervised AI, and deep learning. Simply put, AI is poised to be a game-changer in development work, and participant feedback is just the tip of the iceberg. ?

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* Hisham Jabi is an international development specialist based in Washington, DC. He is the CEO of Jabi Tech Consulting, LLC in Washington, DC and MENA region “www.jabiconsulting.com ” He can be reached at?[email protected]

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