By Sreenivasa Valluri, Principal Enterprise Architect
Rise and shine. It’s time to start the day. You check the weather forecast—do I need an umbrella or sunscreen today? Rain boots or a snow parka?
We all know that forecasts are not always reliable. As the joke goes:
“What does everyone listen to but never believe?”
“The weather forecast.”
No one wants their AI model to resemble a spurious weather report. While we may not have control over the weather, we do have the ability to positively impact AI model reliability.
In this blog, we’ll explore AI model reliability, factors affecting reliability, and what federal agencies should prioritize when designing and deploying AI models.
Avoiding Clouds on the Horizon: AI Model Reliability
For federal missions, the stakes are high. Sure, an inaccurate weather forecast can disrupt your day or maybe ruin your favorite pair of shoes. But for national security missions, an unreliable AI model can introduce false positives or negatives, leading to the misidentification of threats. Similarly, for federal health missions, an unreliable AI model could perpetuate bias, negatively impacting research and care.
So, what is AI model reliability, exactly? AI model reliability refers to the consistency and accuracy of an AI model’s performance over time.
When designing AI models, reliability doesn’t happen by accident. Multiple factors affect AI model reliability, from data quality and drift to model complexity, training methodology to evaluation metrics.
Also Read: How AI Works: the Belly and the Brain
So, how do agencies improve AI model reliability? At Unissant, we focus on these strategies.
- Data Cleaning and Preprocessing: AI models benefit from clean and bias-free data inputs. Unissant is helping clients create synthetic data to address bias and improve reliability (For a simple explanation of profile-based synthetic data generation, see our previous blog Fake It Till You Make It). Additionally, we use preprocessing techniques like normalization and feature engineering to enhance model performance and reduce bias.
- Cross-Validation: This process helps gauge how well a model generalizes to unseen data, much like validating forecasts with real-world conditions. (Learn more about Generalizability in our previous AI in Plain English blog Is Your AI Algorithm Big Game Ready?) Cross-validation techniques divide the dataset into training and validation sets to assess a model's performance on diverse data.
- Explainability: Explainable AI (XAI) helps build trust in the reliability of AI systems, providing visibility into how the AI system arrives at its decisions. XAI is an important concept for identifying and addressing bias. (We break down the essential elements of XAI in Explaining Explainability: Feeding your hunger for responsible AI).
- Regularization and Hyperparameter Tuning: Agencies need to prevent "overfitting," where a model performs well on training data but poorly on new data. For example, if a model is only taught to recognize rain as bad weather, it might not understand that snow or hail also fit into the “bad weather” category. We use regularization to help the model learn more general patterns; hyperparameter tuning fine-tunes the model's settings to find the best balance between being too specific and not specific enough.
- Ensemble Methods: Combining multiple models can improve reliability. Ensemble methods leverage the collective wisdom of multiple models to reduce variance and improve predictive accuracy—think multiple top meteorologists working together to analyze data and make predictions.
- Continuous Monitoring and Retraining: Keep an eye on model performance, assessing how it performs as new data becomes available. Regularly monitoring model performance helps identify drift or degradation over time. Retraining models with new data ensures they remain accurate and up to date, adapting to changing conditions and improving their ability to handle evolving challenges.
Predicting Clear Skies with Confidence
AI model reliability, much like weather forecasting, requires continuous attention, adaptation, and transparency. By incorporating Unissant’s strategies into the lifecycle, agencies can improve AI model reliability. Prioritize these strategies to deliver more reliable, trustworthy predictions, enhancing decision-making and reinforcing public trust. In conclusion, think of a well-built AI model as a sturdy shelter in a storm—it provides valuable protection when you need it most.
