AI in Aid The Next Generation of Financial Black Boxes is Already Here
Published on: Mon May 20 2024 by Ivar Strand
AI in Aid: The Next Generation of Financial Black Boxes is Already Here
For several years, the central challenge in the assurance of financial systems has been the “black box” problem: core processes are executed by software whose logic is often opaque to those responsible for oversight. As the international development community is still developing the frameworks to manage this reality, a new and far more complex generation of black boxes is already being deployed.
These new systems are driven by Artificial Intelligence (AI) and Machine Learning (ML). They promise to bring new efficiencies to fraud detection, grant management, and risk assessment. They also present a profound challenge to our existing models of governance, audit, and accountability.
From Deterministic Rules to Probabilistic Models
The key challenge is a fundamental shift in how these systems operate. Traditional software runs on deterministic logic: if a specific, pre-programmed condition is met, then a specific, pre-programmed action occurs. An auditor, at least in principle, can trace this codifiable logic.
Machine Learning models, however, are probabilistic. They are not explicitly programmed with rules but are trained to recognize patterns in vast datasets. An ML system might flag a transaction as having a “92% probability of being fraudulent” based on a complex web of correlations that are not reducible to a simple, human-readable rule. The logic is emergent, not designed, and can be opaque even to the data scientists who built the model.
The Governance and Assurance Challenge for Aid
Our current assurance frameworks are not designed for this probabilistic reality. The governance questions we already struggle with for simple systems become exponentially more difficult when applied to AI.
- Opacity and Explainability. How can an organization fulfill its fiduciary duty to a donor if it cannot provide a clear, rational explanation for why a significant financial decision was made? If the system that made the decision cannot explain its own reasoning, a critical link in the accountability chain is broken.
- Algorithmic Bias. An ML model trained on historical financial data can inherit and amplify latent biases within that data. An AI designed to predict project success could, for example, systematically down-score proposals from certain geographies or types of local partners based on historical patterns, entrenching inequities. Auditing for this type of bias is a non-trivial challenge.
- Accountability for Error. If a self-learning algorithm misallocates millions of dollars in aid funding, who is ultimately accountable? Is it the organization that deployed the AI, the vendor that provided the underlying model, or the historical data on which it was trained? Our legal and contractual frameworks are not yet prepared for this question.
A Path Forward: Principles for Trustworthy AI
This challenge does not mean we should reject innovation. It means we must proceed with a clear-eyed view of the risks and establish new principles for governance.
As a community, we must insist upon and invest in the field of Explainable AI (XAI). This is a branch of AI research focused on developing techniques that allow opaque models to provide clear, understandable justifications for their outputs. For any AI system used to make a material fiduciary decision, a human-readable explanation should be considered a mandatory output.
Furthermore, we must adapt our governance models. Every AI system must have a designated human owner who is explicitly accountable for its performance, its ethical implications, and the outcomes it produces. Passive trust in an algorithm is not an acceptable governance stance.
The principles of rigorous, independent verification are more critical than ever. The mandate to turn raw data into actionable insight requires that we apply our most stringent scrutiny not just to the outputs of these new intelligent systems, but to their very logic and learning processes. The black box is becoming more complex; our methods of opening it must evolve accordingly.
