Global explainer
Summary: Global explainer treats an AI model as a whole by using a set of data instances to produce explanations to understand the general behavior of the AI model.
Type of pattern: Product pattern
Type of objective: Trust
Target users: Data scientists
Impacted stakeholders: UX/UI designer, RAI governors, AI users, AI consumers
Lifecycle stages: Design
Relevant AI ethics principles: Explainability
Context: The black box nature of AI systems can be a significant challenge to their adoption and raises a number of ethical and legal concerns. One of the main reasons for this is the complexity of the models used in AI systems, particularly Deep Neural Networks (DNNs), which have a large number of parameters that can them difficult to understand. This lack of explainability poses barrier to widespread adoption of AI, as users may be hesitant to trust the suggestions given by AI systems.
Problem: How can we help users understand the general behavior of an AI model?
Solution: A global explainer can help users understand the general behavior of an AI system by using a set of data instances to produce explanations. These explanations provide an overview of the model’s behavior by visualizing the relationship between the input features and the model’s output over a range of values. Global surrogate models, such as tree-based models or rule-based models, can be used to understand the complex AI models as they have inherent explainability, allowing the output decisions to be traced back to their source.
Benefits:
- Better understanding: Global explanations simplify complex AI models by reducing them to linear counterparts, which are easier to understand.
- Improved transparency: Global explanations provide a general understanding of how an AI model behaves, which can help increase transparency and build trust in the AI system.
Drawbacks:
- Limited understandability: Global explanations can be difficult for AI users without technical expertise to understand and provide feedback on.
- Limited specificity: While global explanations provide a general understanding of the model’s behavior, they may lack the specificity required to understand why a specific decision was made for a particular input.
- Lack of accuracy: Global explanations are based on a set of data instances, which can introduce uncertainty and noise, leading to explanations that may not be entirely accurate.
Related Patterns:
- Local explainer: The focus of global explanations is on the whole AI model, while local explanations only consider the individual decision.
- XAI interface: Global explanations can be incorporated into the interface design to allow AI users to understand AI systems’ global behaviors.
Known Uses:
- IBM AI Explainability 360 is a toolkit that contains ten explainability methods and two evaluation metrics for understanding data and AI models. The explainability methods support five types of methods, including data explanations, directly interpretable, self-explaining, global post-hoc, and local post-hoc.
- Microsoft InterpretML is a python toolkit that includes XAI techniques developed by Microsoft and third parties to explain AI model’s overall behavior and the reasons behind the individual decisions.
- EthicalML-XAI provides global explanations by visualizing the behaviors of AI models in terms of input variables.
- tf-explain provides insights to neural networks’ global behaviors by visualizing activations of neurons.