Is “AI-Driven” M&E Creating a Black Box of Bias?

As Artificial Intelligence (AI) rapidly transforms monitoring and evaluation (M&E) processes, the promises of efficiency and innovation are hard to ignore. However, the adoption of AI isn’t without significant challenges. One of the most pressing concerns is the "black box" nature of many AI systems, which can inadvertently perpetuate bias and reduce accountability. This issue isn't just theoretical—real-world examples highlight the very real consequences of AI systems amplifying historical prejudices, potentially undermining the fairness and effectiveness of development programs.

1. The Danger of Bias in AI Training Data

AI systems rely heavily on historical data, which means they can inherit past biases. If the data used to train an AI system reflects societal inequities, the system will likely perpetuate those biases. Take lending algorithms as an example: studies have shown that AI-driven tools, trained on biased historical lending data, disproportionately flag loan applications from minority-owned businesses as "high-risk." A striking example is the disproportionate denial of loans to Black and Latino applicants, even when factors like creditworthiness are accounted for. These biases are similar to the discriminatory practices uncovered in the 2019 The Markup study on mortgage algorithms.

2. The Transparency Problem: AI as a Black Box

Many AI models, especially deep learning systems, are inherently opaque. This lack of transparency means stakeholders often have no way to understand how decisions are made or challenge potentially harmful conclusions. The documentary Coded Bias highlighted how facial recognition software often fails to accurately identify people of color—researcher Joy Buolamwini found that the software misidentified her face unless she wore a white mask. These issues underscore a fundamental problem: when decision-makers can't fully understand how AI reaches its conclusions, they risk accepting flawed decisions without proper scrutiny.

3. Correlation Doesn’t Equal Causation

AI systems are designed to identify correlations, but they don’t always distinguish between correlation and causation. For example, an AI system used by an NGO might find a correlation between the NGO’s presence and improved school performance. However, the system might erroneously attribute success solely to the NGO’s efforts, overlooking other important factors such as community support or external interventions. This kind of misplaced attribution can result in inefficient resource allocation and misguided policy decisions.

4. AI Amplifying Existing Inequalities

When AI is used to allocate resources or determine eligibility for programs, any inherent bias in the system can amplify existing inequalities. A disturbing example of this is the case of Mary Louis, whose application for housing was rejected due to a low score from an AI-powered tenant screening tool called SafeRent. Despite her strong rental history and voucher, the algorithm marked her as a high-risk tenant based on flawed data, sparking a lawsuit involving hundreds of Black and Hispanic tenants. This case led to a $2.3 million settlement and a temporary ban on such scoring systems for voucher users—highlighting how AI in housing can reinforce discriminatory practices and limit access to vital resources.

5. The Illusion of Objectivity: AI in the Criminal Justice System

One of the biggest misconceptions about AI is that it's inherently objective. In reality, AI systems can hide or even amplify human biases, leading to misleading conclusions. In the criminal justice system, risk assessment tools like COMPAS are used to guide pretrial decisions. However, investigations by ProPublica revealed that COMPAS often assigns higher risk scores to Black defendants than to white defendants, even when their backgrounds are similar. This selective use of AI results in biased judicial decisions, showing that the supposed objectivity of AI can obscure systemic biases that still exist in the real world.

6. Data Security and Privacy Concerns

AI-driven M&E systems often require access to vast amounts of personal data, which poses significant privacy risks. For example, AI systems used in educational settings to monitor student performance could expose sensitive student data if security measures aren’t properly implemented. Inadequate safeguards could lead to data breaches, and privacy violations, and erode public trust in the institutions using these systems. Ensuring strong data security protocols is crucial to maintaining the integrity of AI systems and protecting individual privacy.

Conclusion: Moving Forward with Caution

While AI holds immense potential to enhance M&E processes, it is crucial to address the biases, opacity, and ethical concerns that come with it. We must ensure that AI systems are transparent, accountable, and free from harmful biases. This means demanding clearer explanations of how decisions are made, scrutinizing correlations, mitigating inequalities, and protecting data privacy. Only by taking these steps can we harness AI’s potential without amplifying societal biases.