Deep neural networks have achieved remarkable remarks in the past few years in complex processes like image recognition, natural language processing, and game playing. These are often regarded as ‘black boxes’ since they are opaque and the process of how they arrived at the output is unknown. It is important to comprehend how these models make predictions to build trust, enhance reliability, and obtain insights.
To resolve this issue, the ‘explainability techniques’ were introduced which were designed to show how the model made the prediction and which features contributed significantly to the prediction.
These techniques are also called ‘Explainable AI (XAI)’ which is a vital approach to enhance the understanding of AI models. Interpretable explanations are obtained using XAI benefiting the users, operators, and developers. Through these techniques, the users can comprehend the reasoning behind the prediction encouraging broader adoption of AI technologies across various fields. There are two types: sensitivity analysis, which identifies the input factors that contribute the highest locally, and decomposition which show how the output is distributed among the input factors.
One of these decomposition techniques is ‘Deep Taylor Decomposition’ which is an advanced method used to make the prediction process of deep neural networks transparent. Choosing the best rules and parameters for these decomposition methods is tough. The iterative process of making the decomposition model more complex by adding more layers and restraining the input is called ‘Taylor Decomposition.
This method breaks down the decision-making process and provides insights into which parts were the most important.
The technique of Deep Taylor Decomposition is based on Taylor decomposition. It is generally used in calculus to approximate functions using their derivatives. But it has limitations in deep learning due to the highly non-linear nature of neural networks.
The realization that the function learned by the neural network can be divided into smaller subfunctions led to the deep Taylor decomposition method. In this method, attribution scores can be given to the features of the inputs based on their contribution to the output.
DNNs have high representational power but it comes at the cost of interpretability. But by using deep Taylor decomposition the models can be interpreted through attributions as mentioned above.
By the analysis of the contributions of the features, insights can be gained into how the model processes the information which is important for debugging and in the improvement of the model’s performance.
The deep Taylor decomposition method can be used by first training a DNN using labelled data. Then this method can be applied which would traverse the network backwards from the output layer calculating the relevance scores of the features at each layer with higher contributions getting higher scores. This is done by backpropagating from the output layer the relevance scores. The importance scores can be visualized using visualization methods like heatmaps or saliency maps which highlight the most significant features.
It offers a connection between the propagation rule and the input domain allowing the construction of new rules based on different circumstances.
The input domain refers to the possible input data that the neural network can process. This is achieved by considering the relevance scores as a product of the neuron’s activation and a constant term. By assuming this structure for relevance, it is possible to analyze the rule in a more general context.
Being able to construct new rules depending on the types of data is a huge advantage. By just fine-tuning the rules the researchers can interpret the predictions and the model behaviour better. This encourages domain-specific interpretability techniques.
Deep Taylor decomposition is a powerful technique that unlocks the potential for DNNs with transparency and interpretability, thus building trust and improving the model’s performance. This method can be used across various domains from healthcare finance. This method has a lot of scope in the future as it is still an ongoing area of research. This bridges the gap between the capabilities of deep learning models and the need for interpretable decision-making.
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T Lalitha Gayathri