The Role of Domain Knowledge in Machine Learning: Why Subject Matter Experts Matter

Machine learning (ML) is considered the largest subarea of artificial intelligence (AI), studying the development of software systems that learn from data by themselves to perform a task, without being explicitly programmed with the instructions to address it. Its significance has grown considerably across various fields, from healthcare and finance to retail and manufacturing, revolutionizing how we use technology to solve complex problems.

As crucial as data are for training and performing inference processes with ML systems, there is the common misconception that data alone is sufficient in real-world domains. However, domain knowledge — understanding the specific nuances, constraints, and context of the field in question — is crucial for framing the right problem, and ensuring that the model’s predictions are relevant, interpretable, and actionable in the application domain.

Importance of Domain Knowledge: What Happens When They Are Overlooked?

How does domain knowledge enhance ML system lifecycle stages like problem framing, data understanding, and model interpretation? As stated, domain knowledge provides critical insights that guide the entire ML process, ensuring that the models developed are relevant and effective in their intended applications.

But there is one more big factor. Just like domain knowledge itself, the involvement of subject matter experts (SMEs) is equally critical in the lifecycle of building an ML solution. Let’s examine their relevance in different stages:

• Data Collection: SMEs help identify the most determinant data sources and guarantee the data gathered faithfully represents real-world conditions
• Identifying Relevant Features: SMEs can pinpoint the most important features, reducing noisy ones and enhancing model performance by focusing on key drivers of model outcomes
• Model Validation and Interpretation: SME’s expertise helps validate model outputs, ensuring that the predictions meet real-world expectations and making sense of complex outcomes
• Avoiding Biases: SMEs are pivotal in recognizing and mitigating biases that may remain unnoticed by data scientists, thereby fostering fair and balanced model outcomes
• Ensuring Realistic Outcomes: by incorporating SME insights, ML models can be calibrated to reflect the nuances of real-world settings, ensuring practical and actionable predictions

The key to successfully involving SMEs at these stages is to promote open communication and prioritize collaboration with data scientists and engineers: mutual collaboration is instrumental in integrating domain expertise seamlessly into the entire ML workflow.

But what pitfalls arise when domain knowledge and SME involvement are overlooked? The consequences include biased models that struggle to generalize to future data, incorrect interpretations of data leading to misguided decisions, and ultimately, project risks and failures that could have been prevented with the right expertise roles on board.

Domain Knowledge in Action

Below are three examples of integrating domain knowledge and SMEs in ML workflows, and their importance.

In healthcare, developing a predictive model for patient readmissions usually requires the participation of medical SMEs participation to help identify critical clinical variables like lab results and medication histories. Their involvement contributes to building a model that showcases real patient risks rather than mere statistical patterns.

In finance, concretely for fraud detection problems, financial analysts can guide the model training and fine-tuning processes by highlighting transaction behaviors that truly represent fraud, for instance, unusual purchase patterns or sudden significant account activity, refining the model’s accuracy beyond more generic anomalies that may end up leading to unnecessary false positives.

Finally, in manufacturing industrial engineers can leverage their domain knowledge to optimize ML systems used in the predictive maintenance of factory equipment. Their ability to determine which sensor data, such as vibration or temperature, are most relevant, is crucial in improving the system’s ability to predict failures of machinery and reduce downtime.

Conclusion

The importance of combining ML system development with domain expertise is evident in achieving more accurate, reliable, and context-aware models. In these scenarios, it is worth underscoring the value and significance of subject matter expert insights in refining data collection, guiding feature selection, interpreting results, and ensuring the overall success of the ML project.

For your next ML project, don’t forget to consult an expert, and you can see firsthand how their involvement can buoy its outcomes.