How can ANN research and innovation contribute to explainable AI development?

1 XAI concepts and goals

XAI is not a single technique or approach, but a broad term that encompasses various concepts and goals related to making ANNs more explainable. Transparency, for instance, allows for the internal structure, logic, and parameters of an ANN to be revealed and understood; interpretability provides meaningful and intuitive explanations of an ANN's outputs, inputs, and features; accountability ensures that an ANN is responsible for its actions and outcomes; and trustworthiness establishes confidence in an ANN's functionality, quality, and ethics. Together, these concepts and goals help align an ANN's objectives and values with those of its users and stakeholders.

2 ANN challenges and limitations

ANNs are often considered as black boxes, meaning that their inner workings are hidden and complex. This presents several challenges and limitations for XAI, such as complexity due to the millions of parameters, layers, and connections; nonlinearity which makes it hard to identify and isolate the causal factors and effects of their decisions; variability caused by different architectures, designs, and settings; and uncertainty due to errors, biases, and uncertainties in their data, models, and predictions. These issues make it difficult to trace how ANNs process information and learn from data, challenging to compare and evaluate their performance and robustness across different scenarios and tasks, as well as risky to rely on their outputs without validation and verification.

3 ANN research and innovation

Research and innovation in artificial neural networks (ANNs) can contribute to the development of explainable AI (XAI) by exploring and proposing new methods and techniques to address the challenges and limitations of ANNs. Visualization tools can be used to display and explore the structure, behavior, and results of an ANN, as well as highlight its salient features, patterns, and anomalies. Additionally, mathematical and statistical methods can be used to measure the importance and contribution of each input, feature, or parameter of an ANN to its output or decision. Analytical and logical methods can be used to extract the rules, concepts, or functions that an ANN has learned from its data or model. Finally, empirical and experimental methods can be used to test the accuracy, reliability, and fairness of an ANN's outputs or decisions, as well as identify and correct its errors, biases, and uncertainties.