Proxy labels are alternative labels used in machine learning and AI when the true labels of data are unavailable, hard to obtain, or too costly to collect. Instead of directly labeling data with the target variable of interest, proxy labels are used as a stand-in, ideally capturing information that is closely correlated with the true label. This approach can be a practical solution in many real-world scenarios, especially when working with large datasets or proprietary information.
For example, imagine developing a model to predict customer satisfaction, but you lack direct survey data. You might use the number of customer support tickets as a proxy label, assuming that higher ticket volumes roughly correspond to lower satisfaction. In medical AI, proxy labels might be used when a definitive diagnosis is unavailable, so a related clinical event, such as a specific procedure or medication, is used as a stand-in for the condition of interest.
Proxy labels are common in weak supervision, self-[supervised learning](https://thealgorithmdaily.com/self-supervised-learning), and semi-[supervised learning](https://thealgorithmdaily.com/semi-supervised-learning). They help researchers and practitioners build and validate models without the resource-intensive process of manual annotation. However, the choice of proxy label is critical. If the proxy is poorly correlated with the true label, it can introduce significant bias or noise into the model, undermining its performance and reliability.
The use of proxy labels involves a trade-off between data availability and label quality. While they can unlock new possibilities for model training where labeled data is scarce, it’s important to validate and, if possible, quantify the relationship between the proxy label and the true variable. This validation can involve statistical analysis, domain expertise, or additional smaller datasets with true labels for comparison. Failure to do so can result in models that make systematic errors or fail to generalize to real-world scenarios.
Proxy labels also play a role in fairness and ethics. In some cases, a proxy label may inadvertently encode sensitive or protected attributes (such as using zip code as a proxy for socioeconomic status), potentially leading to unintended biases in the model outcomes. Practitioners should be mindful of these risks and consider them in the design and evaluation of AI systems.
In summary, proxy labels serve as a useful tool for training and evaluating AI models in the absence of true labels. They enable progress when direct supervision is unattainable but require careful selection, validation, and ethical consideration to ensure robust and fair AI systems.
