How can ANN research improve the interpretability of neural networks?

1 What is interpretability?

Interpretability is the ability to explain the logic, behavior, and outcomes of a system or model in a comprehensible way. For neural networks, interpretability means being able to answer questions such as: What features or patterns does the network learn? How does the network process the input and produce the output? What are the sources of uncertainty or error in the network? Interpretability can help users, developers, and regulators to understand, trust, and improve neural networks.

2 Why is interpretability important?

Interpretability is important for several reasons. First, it can help users to trust and accept the decisions or recommendations made by neural networks, especially when they affect human lives or well-being. Second, it can help developers to debug, optimize, and validate neural networks, and to identify potential biases, errors, or limitations. Third, it can help regulators to monitor, audit, and evaluate neural networks, and to ensure that they comply with ethical, legal, and social standards.

3 How can ANN research improve interpretability?

ANN research can improve interpretability by developing new methods, techniques, and tools that can reveal the inner workings and reasoning of neural networks. These methods can be classified into two main categories: post-hoc and intrinsic. Post-hoc methods analyze the network after it has been trained or deployed, and try to extract or visualize the features, weights, activations, or gradients that influence the network's behavior. Intrinsic methods design the network in a way that makes it more interpretable by default, such as by using sparse, modular, or symbolic architectures, or by incorporating prior knowledge or constraints.

4 What are some examples of post-hoc methods?

Post-hoc methods are techniques used to gain insight into the inner workings of a neural network. Saliency maps, for example, are heatmaps that identify the regions or pixels of an input that contribute the most to the network's output. Feature visualization images are generated to maximize the activation of a specific neuron or layer in the network, and show what kind of patterns or concepts the network has learned. Attribution methods are also used to assign a score or value to each input feature or neuron, based on its importance or relevance for the network's output. These methods can be based on gradients, perturbations, game theory, or causal inference.

5 What are some examples of intrinsic methods?

Intrinsic methods can be used to improve the performance and interpretability of neural networks. Sparse coding forces the network to learn a sparse representation of the input, meaning that only a few neurons are active at a time. This can make the network more efficient and robust, as each neuron can represent a distinct feature or concept. Modular networks consist of multiple sub-networks or modules that perform different tasks or functions, and communicate with each other. This can make the network more flexible and adaptable, as each module can have a clear role and responsibility. Symbolic networks use symbolic logic or rules to represent and manipulate knowledge, rather than numerical values or weights. This can make the network more transparent, explainable, and interpretable, as each symbol can have a meaningful and consistent interpretation.