The latest fashion in model building is adding AI/ML (Artificial Intelligence/Machine Learning) technology to numerical models for weather forecasting.  No doubt soon there will be climate models also claiming improved capability by doing this.  A meteorological example is called Aardvark Weather and a summary is provided at Tallbloke’s Talkshop Scientists say fully AI-driven weather prediction system delivers accurate forecasts faster with less computing power.

Like all inventions there are weaknesses along with the claimed benefits.  Here’s a short list of the things that can go wrong with these new gadgets. The concerns below are listed along with some others in a paper Understanding the Weaknesses of Machine Learning: Challenges and Limitations by Oyo Jude. Excerpts in italics with my bolds.

Introduction

Machine learning (ML) has become a cornerstone of modern technological advancements, driving innovations in areas such as healthcare, finance, and autonomous systems. Despite its transformative potential, ML is not without its flaws. Understanding these weaknesses is crucial for developing more robust and reliable systems. This article delves into the various challenges and limitations faced by ML technologies, providing insights into areas where improvements are needed

Data Quality and Bias

Data Dependency

Machine learning models are highly dependent on the quality and quantity of data used for training. The performance of an ML model is only as good as the data it is trained on. Common issues related to data quality include:

Incomplete Data: Missing or incomplete data can lead to inaccurate models and predictions. Incomplete datasets may not represent the full spectrum of possible inputs, leading to biased or skewed outcomes.
Noisy Data: Noise in data refers to irrelevant or random information that can obscure the underlying patterns the model is supposed to learn. Noisy data can reduce the accuracy of ML models and complicate the learning process.

Data Bias

Bias in data can significantly impact the fairness and accuracy of ML systems. Key forms of data bias include:

Selection Bias: Occurs when the data collected is not representative of the target population. For example, if a model is trained on data from a specific demographic group, it may not perform well for individuals outside that group.
Label Bias: Arises when the labels or categories used in supervised learning are subjective or inconsistent. Label bias can skew the model’s understanding and lead to erroneous predictions.

Model Interpretability and Transparency

Complexity of Models

Many advanced ML models, such as deep neural networks, are often described as “black boxes” due to their complexity. The lack of transparency in these models presents several challenges:

Understanding Model Decisions: It can be difficult to understand how a model arrived at a specific decision or prediction, making it challenging to diagnose errors or biases in the system.
Trust and Accountability: The inability to interpret model decisions can undermine trust in ML systems, particularly in high-stakes applications such as healthcare or criminal justice. Ensuring accountability and fairness becomes challenging when the decision-making process is opaque.
Explainability:  Efforts to improve model interpretability focus on developing techniques and tools to make complex models more understandable. Techniques such as feature importance analysis, surrogate models, and visualization tools aim to provide insights into model behavior and decisions. However, achieving a balance between model performance and interpretability remains an ongoing challenge.

Generalization and Overfitting

Overfitting

Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise, resulting in poor performance on new, unseen data. This issue can be particularly problematic with complex models and limited data. Strategies to mitigate overfitting include:

Cross-Validation: Using techniques like k-fold cross-validation helps assess model performance on different subsets of the data, reducing the risk of overfitting.
Regularization: Regularization methods, such as L1 and L2 regularization, add penalties to the model’s complexity to prevent it from fitting noise in the training data.

Generalization

Generalization refers to a model’s ability to perform well on unseen data that was not part of the training set. Achieving good generalization is crucial for the practical application of ML models. Challenges related to generalization include:

Domain Shift: When the distribution of the data changes over time or across different domains, a model trained on one dataset may not generalize well to new data. Addressing domain shift requires continuous monitoring and updating of models.
Data Scarcity: In scenarios where limited data is available, models may struggle to generalize effectively. Techniques such as data augmentation and transfer learning can help address data scarcity issues.

Comment:

Many similar issues have been raised against climate models, undermining claims their outputs are valid projections of future climate states.  For example, the issue of detailed and reliable data persists.  It appears that even the AI/ML weather forecasting inventions are dependent on ERA5, which has a record of only ~40 years to use for training purposes.  I’m suspending belief in these things for now–new improved black boxes sound too much like the Sorcerer’s Apprentice.