Blog: Practical Approaches to Tackle Common Real-World AI Pitfalls

Applying artificial intelligence in the real-world settings extends far beyond the controlled environment of academic research, Kaggle competitions, or personal experiments with prototype models in popular Python AI/ML frameworks. When AI steps out of the lab and into our daily lives, the stakes rise significantly due to the real-world consequences its applications can entail. 

While AI technology has become readily accessible and most organizations possess some form of data, crafting a quick solution can often seem deceptively simple. Yet, advancing these solutions by integrating an AI model or system into a live operational environment typically involves a labyrinth of complexities. 

The team at Oxide AI has compiled some common challenges encountered in real-world AI deployments, along with insights on how to navigate these hurdles. It is important to note that this overview is not exhaustive; every industry presents its unique set of challenges.  

In this blog post, we focus entirely on the real-world applications of AI models, saving the technical operations for another post. While there are many other perspectives to consider, such as responsible, ethical, and environmental aspects, our insights provide a solid foundation for understanding the essential considerations in nearly every AI implementation. 

1) Model Overfitting 

A machine learning model that excels at interpolation might be overfitting, performing very well on training data but poorly on unseen data. This suggests that the model has learned the specific details and noise of the training data, rather than capturing the underlying general patterns necessary for effective extrapolation. 

What to do: 

• Validate and analyze the model carefully if it produces “too good to be true” results. 
• Test it with more varied data or add noise to your input data and measure the response. 
• Use more training data, if possible, to help the model learn more generalized patterns. 
• Perform early stopping during training to prevent the model from learning the noise in the training data. 
• More advanced: hyperparameter tuning, random dropout, regularization, cross-validation on different subsets of the data. 

2) Unpredictability in Extrapolation 

Interpolation enables neural networks to predict within the range of the training data, and generative AI (like LLMs) models can indeed be quite powerful in predicting outcomes based on learned data distributions.  

However, when neural networks extrapolate, they attempt to predict based on patterns beyond previous observations. This can lead to unpredictable or completely incorrect outputs, as the network essentially “guesses” relationships in regions outside its training data. In critical sectors, such as healthcare or autonomous driving, erroneous extrapolations can result in severe consequences.  

The challenges of relying on deep learning in finance automation also stem from this issue. With inputs constantly evolving beyond training data, future predictions based solely on historical data for training become less viable.  

What to do: 

• If a solution handles highly varied input data, consider replacing neural networks with more efficient model-based predictions, facilitating easier inspection and understanding of the model. 
• Use ensemble methods to combine predictions from multiple models to improve robustness and reliability. 
• Leverage a proxy model (often referred to as an “oracle”) to validate predictions at scale. 
• Introduce heuristic rules to capture the “soundness” of the results produced. 
• Implement continuous monitoring to identify and address drift in model performance over time. 
• Introduce a triage system to route uncertain outputs to human review, based on some type of confidence score reflecting how certain the output is. 
• Always log inputs and results produced to enable a thorough review of edge cases.

3) Inherent Biases 

Both interpolation and extrapolation are susceptible to biases inherent in the training data. When training data is biased, predictions—whether within the data range (interpolation) or extending beyond it (extrapolation)—are likely to reflect and potentially amplify those biases. 

What to do: 

• Ensure the training dataset is representative of the real-world scenarios to mitigate biases. 
• Conduct regular audits of model predictions to assess and address biases effectively. 
• Utilize fairness metrics to evaluate and enhance the model’s performance across different groups. 
• Complement real data with synthetic data to ensure representation of underrepresented groups and scenarios. 
• Institute a formalized bias mitigation workflow that includes steps for data collection, bias detection, bias addressing, and validation. 
• Establish transparency in the modeling process by documenting data sources, model choices, and mitigation strategies, making it easier to identify and correct biases. 
• Use explainable AI (XAI) where possible to provide full transparency, especially for numerically oriented domains.   
   

4) Underestimating the Complexity of Real-World Problems 

Many real-world AI problems involve high levels of complexity that go beyond what can be handled through simple interpolation or extrapolation. These scenarios demand an understanding of intricate causal relationships, contextual nuances, and multi-dimensional data, aspects that often lie beyond the capabilities of current AI models. 

What to do: 

• Understand the data thoroughly to prevent models from capturing spurious correlations. 

• Collect comprehensive surrounding data to uncover valuable properties or interactions. 
• Perform scenario analysis and stress testing to see how the model behaves under various conditions and to identify potential weaknesses. Baseline testing with pure randomness is always a good practice. 
• Model and simulate systemic and macro-level interactions to manage large error margins stemming from external changes. 
• Regularly update the model using new data and retrain it to reflect recent trends and changes in the problem space. For domains where information is under a lot of change, leverage continuous model adaptation based on optimization rather than cyclical retraining.  
• Use advanced techniques like transfer learning to build robust models that operate well in varying real-world environments.