Model Explainability: Striking the Balance Between Performance and Transparency

In today’s world, where automated systems make critical decisions in fields like healthcare, finance, and law, explainability in machine learning (ML) has become a cornerstone of ethical AI development. While cutting-edge models like deep neural networks often achieve remarkable performance, they frequently operate as black boxes, leaving decision-making processes opaque. This lack of transparency is increasingly a concern, especially in high-stakes applications where understanding "why" a decision was made is as important as the decision itself.

The Challenge of Black-Box Models

Deep neural networks (DNNs) excel at tasks like image recognition, natural language processing, and recommendation systems. Their power lies in their complexity—millions of parameters and layers that extract patterns from data. However, this complexity makes it difficult to trace the logic behind their decisions. Techniques exist to probe these models, such as analyzing feature importance by observing weight activations during forward propagation. Yet, these methods are speculative and often fail to provide a comprehensive picture.

Attention Mechanisms: A Glimpse of Clarity?

Not all neural network-based models are entirely opaque. Models with attention mechanisms, like those used in modern NLP systems, offer some degree of interpretability. These mechanisms assign varying importance to different input features, learning these weights during training. This allows us to visualize how the model focuses on specific parts of the data when making decisions. In a nutshell, attention helps to create a more contextualized representation of an input before propagating it through the model to make a prediction. It weighs each token in an input by all other tokens in the same input to create representations that depend on the context, i.e. the other tokens around it. This way, when we try to make a prediction, we have dynamic representations for each token that get affected by the surrounding tokens, giving the model flexibility to ‘understand’ tokens in different contexts.

In NLP, these attention maps can reveal how a model learns to prioritize syntax or grammar rules in earlier layers. However, as the model dives deeper into abstraction, these patterns become harder to interpret, often appearing random to human observers. This trade-off between interpretability and complexity is a persistent challenge for neural networks.

Let’s take a very simple example to understand how we can interpret attention at an early layer in LLM models. In the following image, we have some example sentences and a given word from the input sequence in the left column, we can see that for the word ‘Screen’ in the input sentence “The screen looks great. But the battery life is too short”, the attention weight assigned the most importance to the word ‘great’, because the model learned through many iterations during training, that in this kind of context ‘Screen’ and ‘great’ are closely related. Similarly we have the word ‘battery’ being strongly associated with ‘short’ in the next example.