One of the most fascinating and contentious areas of artificial intelligence research right now, which you may not have heard of, is deep learning explainability — the ability of an algorithm or model to provide human-comprehensible explanations for its decision-making processes. Explainability is especially important in the context of deep learning because deep neural networks frequently function as black boxes, making decisions without revealing how they came to those decisions.
If, by some miracle, you haven’t yet heard about deep learning, let’s take a moment to talk about what deep learning actually is before we get into the specifics of its explainability.
Deep learning is a subset of machine learning that involves teaching artificial neural networks (that usually have a lot of layers of neurons) to learn from data by training them. These networks can recognize patterns in data that are too complex for conventional machine learning algorithms to handle because of the high dimensionality and complexity that data has.
However, neural networks are modeled after the human brain’s structure and can handle any complexity and dimensionality — depending on how many layers and neurons the network has.
The fact that deep neural networks can be extremely complicated and have a huge number of neurons is a double-edged sword, however, as they are extremely challenging to understand. In fact, many deep learning models are so intricate that even their creators have trouble comprehending how they function.
The safety and reliability of deep learning models have been questioned as a result of this lack of transparency, particularly in high-stakes fields like finance and healthcare.
To address these concerns, researchers have been developing different techniques that can provide explanations for deep neural network decision-making processes. There are two main approaches to explainability: the model-specific approach and the model-agnostic approach.
The goal of techniques that are model-agnostic is to provide explanations for any kind of machine-learning model, regardless of how complex or simple it is.
LIME (Local Interpretable Model-Agnostic Explanations) is a well-known model-agnostic method that works by creating a straightforward, interpretable model that approximates the behavior of the original model in a specific area of the input space.
SHAP (SHapley Additive exPlanations) is another popular model-agnostic method that calculates the contribution of each input feature to the model’s output using a game-theory approach.
On the other hand, techniques that are model-specific are made for deep neural networks and use their particular architecture to provide explanations.
Layer-wise Relevance Propagation (LRP) is one of these methods. It works by sending the relevance of the model’s output back through the network to find out how each input feature affects the final decision.
Grad-CAM (Gradient-weighted Class Activation Mapping) is another model-specific method that examines the gradients of the model’s output in relation to each pixel in the input image to highlight the regions of an image that contributed the most to the decision made by the model.
Issues with Explainability
Both of these explainability methods look promising, but they have some important drawbacks. Model-agnostic methods, for instance, may not offer the same level of insight as model-specific methods and may be computationally expensive. Model-specific methods, on the other hand, may not provide human-friendly explanations and may be difficult to apply to different kinds of deep neural networks.
When it comes to deep learning explainability, there are also ethical and legal considerations to take into account in addition to these technical difficulties. For instance, if a model’s decision is explained, it might reveal private or sensitive information about the data or the people involved. As a result, there have been calls for regulations regarding the use of deep learning models in particular fields that are more critical.
Regardless of these difficulties, the requirement for deep learning explainability is obviously there. Deep neural networks will remain opaque and challenging to comprehend without them.
Conclusion
We can increase the transparency, dependability, and safety of deep learning models and ensure that they are utilized in ways that are beneficial to society as a whole, only by developing and improving explainability techniques further.
By making the predictions of deep learning models easier to understand and trust, explainability for deep learning can also contribute to their increased acceptance and trustworthiness.
Right now, many people are hesitant to use or trust them, because they don’t know how AI-powered technologies work. And, if you really think about it, can you really blame them?
The only way to increase people’s understanding and trust in these scary new technologies is by making deep learning models easier to understand and interpret.
By doing so, we will soon live in a world where Artificial Intelligence will be a trusted companion and friend of humanity, instead of an Arnold Schwarzenegger Terminator robot, here to crush our bones.
