Was sind die effektivsten Methoden, um Entscheidungen neuronaler Netze zu erklären?

In the context of neural networks, local explanations are concerned with explaining specific outputs or decisions for specified inputs or occurrences. They seek to shed light on why a neural network generated a certain classification or prediction, for as identifying an image as a cat or a dog or estimating the value of a home. Saliency maps, which emphasize influential input regions or pixels, and counterfactuals, which show how outputs vary as inputs change, are two common tools for local explanations. They can, for example, illustrate the elements of a house influence its expected price.

Local explanations in the context of neural networks are focused on explaining specific outputs or decisions for given inputs or instances. They aim to provide insights into why a neural network made a particular classification or prediction, such as classifying an image as a cat or a dog or predicting a house's price. Common methods for local explanations include saliency maps, which highlight influential input regions or pixels, and counterfactuals, which demonstrate how outputs change with input modifications. For instance, they can reveal which features of a house affect its predicted price.

The goal of global explanations is to provide a general understanding of a neural network's logic or behavior across all or most inputs and outputs. You may wish to know, for instance, what characteristics or patterns a neural network has become adept at identifying or how it considers various aspects when making judgments. The use of feature importance scores, which quantify the total effect of each input characteristic on the output, is a popular technique for global explanations. Prototype selection is an additional technique that finds the most typical or representative inputs for each output class. For instance, you may like to view the images that a neural network determines to be the most dog- or cat-like.

Global explanations in the context of neural networks seek to provide insights into the network's general behavior and decision-making logic across various inputs and outputs. This involves understanding what features or patterns the network has learned to recognize and how it weighs different factors in its decisions. Feature importance scores are a common method for global explanations, quantifying the overall influence of each input feature on the output. Prototype selection is another approach, identifying the most representative inputs for each output class. For instance, it can reveal which images are most cat-like or dog-like according to the neural network.

Simplifying models is an excellent strategy when prioritizing explainability. Decision tree algorithms, in particular, offer a compelling solution, enabling the extraction of feature importance through the model's splits. Additionally, these models present advantages in production deployment due to their lower resource demands compared to neural networks.

Model simplification entails the creation of a simpler, more interpretable model that approximates a neural network, which can then be used for explanations. Techniques like linear models, decision trees, or rule-based systems are employed to approximate the neural network, and the coefficients, branches, or rules of this simpler model are analyzed. Model simplification can offer both local and global explanations, depending on its training and application. However, it may introduce errors or inconsistencies because the simpler model may not capture all the nuances or complexities of the original neural network.

Model inspection is the practice of analyzing the internal components and parameters of a neural network, including weights, biases, activations, or gradients. This process may involve visualizing filters or neurons in a convolutional neural network or plotting attention weights or embeddings in a recurrent neural network. Model inspection provides insights into how the network processes and represents input data. However, it can be challenging to interpret or generalize because neural networks often use abstract or nonlinear features that are not easily understandable by humans.

Highlight which features or inputs had the most significant influence on the decision. Techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) can help showcase feature importance. Create visual representations of the network's inner workings. For instance, heatmaps can illustrate regions of an image that contributed most to the decision, aiding in image classification tasks.