Artificial intelligence systems today can be extremely complex, with massive neural networks that have been trained on huge datasets. While this approach has led to significant progress in AI, it has also made understanding and explaining the decisions of AI systems a challenge. As AI continues to be deployed for important tasks that affect people’s lives, it is critical that these systems are transparent and their decisions can be explained and understood. Researchers and engineers are actively working on a number of methods to build more transparent and interpretable AI.
One approach is to design AI systems using more interpretable machine learning models. Rather than large neural networks, algorithms like decision trees can be used. Decision trees break down a model’s decisions into a series of if-then logical steps based on features of the input data. Each step in the tree shows how a decision is made based on the value of a specific feature. This makes it relatively easy for humans to understand the process by which the AI model reaches its conclusions. Other interpretable models include rule-based models which use sets of explicit rules to map inputs to outputs, and linear regression which uses a mathematical equation to relate the value of input features to the predicted output. While these models may be less accurate than complex neural networks on very difficult tasks, they provide transparency into how a model works which is critical for high-risk applications.
For systems based on neural networks, techniques like layer-wise relevance propagation (LRP) and deep learning important features (DeepLIFT) have been developed to help explain their decisions. These methods analyze how much each input feature contributed to the network’s output predictions. Features that were more relevant receive a higher “relevance score”. Visualizing these relevance scores helps identify which specific aspects of the input data drove the network’s decision making. Other explanation techniques analyze the network to determine prototype examples that are most representative of each class. Comparing a new input to these prototypes provides insights into why it was classified in a particular way.
Model-agnostic tools have also been created to explain “black box” AI systems. One example is SHAP (SHapley Additive exPlanations) values which assign each feature an importance score representing its average contribution to the model’s output over all possible coalitions it could join. This provides a local interpretation of how much impact changing a feature’s value would have on the prediction. Perturbation-based testing systematically alters features and observes changes in outputs to identify important features. LIME (Local Interpretable Model-agnostic Explanations) approximates any classifier locally with an interpretable model to provide explanations for individual predictions.
Engineers are also exploring ways to design neural networks from the start with interpretability and explainability in mind. Techniques like learning explainable policies use intrinsically interpretable models like decision trees as components within deep reinforcement learning systems. Other approaches find tradeoffs between accuracy and interpretability by imposing constraints during network training that force important intermediate representations to align with patterns humans can understand. There is even research on building neural Turing machines that learn simple algorithms and data transformations, making their decision processes more transparent.
In addition to model-specific techniques, researchers are developing tools like visualization dashboards to help non-technical users understand complex AI systems. Interactive tools allow end users to actively explore model behaviors, test how predictions change under different conditions, and quickly generate human-interpretable summaries of system rationales and limitations. Standardized explanation formats are also being developed to ensure different AI products can provide comparable types of explanations to users.
Making AI transparent and interpretable is an active area of research. The goal is to develop technical methods engineers can use to gain insights into black box machine learning models, and to design new AI systems from the ground up with transparency and interpretability in mind. Continued progress in this field will be critical to build trust in AI and ensure these advanced technologies are developed and applied responsibly.