The Ethics of Automation in Development Finance
Published on: Fri May 19 2023 by Ivar Strand
The Ethics of Automation in Development Finance
The drive to automate financial processes in the international development sector is compelling. The goals—increased efficiency, reduced human error, and the ability to deliver aid at greater scale—are entirely laudable. Yet, this pursuit of operational efficiency introduces a set of profound ethical questions that require careful consideration.
When we design an algorithm that makes a decision to grant or deny a cash transfer to a family in a crisis, what is our ethical responsibility for that automated judgment? In our sector, efficiency cannot be the sole metric of success. The principles of our work demand that we also account for fairness, equity, and due process.
Efficiency vs. Due Process
At its core, the ethical challenge of automation in aid is a tension between two different sets of values. Technology is optimized for efficiency. Development and humanitarian action, however, must be optimized for due process.
Consider a practical example. An automated system for beneficiary payments might be designed to instantly reject any claim where a national ID number contains a typographical error. From a data integrity perspective, this is an efficient rule. For the family whose payment is blocked, however, it is a significant adverse event. If the system does not provide a clear and accessible pathway for that family to correct the error and appeal the decision, it represents a fundamental failure of due process.
An efficient system that is not also fair is not a successful system in our context.
Key Ethical Hazards in Automated Decision-Making
As organizations increasingly rely on automated systems, they must be cognizant of the specific ethical risks this introduces. Our work in monitoring these systems has shown that these risks are non-trivial.
- The Risk of Embedded Bias. Algorithms and models trained on historical data can inherit and amplify the latent biases within that data. An automated system designed to identify “high-risk” communities could inadvertently discriminate against marginalized ethnic groups, systematically excluding them from aid.
- The Problem of Recourse. When a person is denied a benefit by a human official, there is a clear channel for inquiry and appeal. When the denial is issued by an automated system, to whom does the beneficiary turn? A “black box” algorithm offers no explanation and no obvious path to recourse, creating a power imbalance that is inconsistent with the principle of beneficiary accountability.
- The Dehumanization of Aid Delivery. Development is a fundamentally human enterprise. Over-reliance on purely automated decision-making risks reducing people to data points to be processed. It can remove the essential element of human judgment, context, and empathy that is often required to navigate complex situations in challenging environments.
Principles for Responsible Automation
The solution is not to reject the benefits of technology, but to deploy it within a robust ethical framework. We advocate for a set of clear principles for the use of automation in aid finance.
- The primary principle should be a “human-in-the-loop” model for all significant, adverse decisions. While systems should automate routine processing, any final decision that denies a benefit or payment to a beneficiary must be subject to review and confirmation by a human official.
- All programs using automated decision-making must establish and publicize a clear, simple, and accessible appeals process for beneficiaries.
- Systems should be subject to regular, independent bias audits to actively test for and mitigate unintended discriminatory outcomes.
Our fiduciary duty as development professionals is not just to our donors, but also to the communities we serve. Technology-driven monitoring, therefore, must be about more than verifying financial flows. It must also act as an ethical safeguard, ensuring our systems are designed and operated in a way that is fair, transparent, and ultimately accountable to the people they are meant to help.
