Black-box and White-Box Models towards Explainable AI
Generating explanations from black-box models using model properties, local logical representations, and global logical representations
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Generating explanations from black-box models using model properties, local logical representations, and global logical representations
Explainable AI (XAI) deals with developing AI models that are inherently easier to understand for humans, including users, developers, policymakers, and law enforcement. Neuro-Symbolic Computing (NSC) deals with combining sub-symbolic learning algorithms with symbolic reasoning methods. Therefore, we can assert that Neuro Symbolic Computing is a sub-field under Explainable AI. NSC is also one of the most applicable approaches since it relies on combining existing methods and models.
Explainability is the ability to meaningfully describe things in a human language. In other words, it is the possibility to map raw information (data) to a meaningful symbolic representation for humans (e.g. an English text)
By extracting symbols out of sub-symbols, we can make these sub-symbols explainable. Both XAI and NSC are trying to make sub-symbolic systems more explainable. NSC is more about mapping sub-symbols to symbols, explainability by design through logic: symbolic reasoning over sub-symbolic learned representations. XAI is a less specific paradigm, and it is more about explainability in all its nuances, even if it is wrapped around unexplainable models. If extracting symbols out of sub-symbols implies explainability, then XAI includes NSC.
Let’s see these NSC with an example:
Mao et al. propose a new NSC model, the Neuro-Symbolic Concept Learner, which follows the following steps:
An AI model can be (i) white-box or (ii) black-box.
Black-box models can be made explainable with
Therefore, here is the diagram showing the sub-categories of AI models in terms of their explainability:
Apart from the logical differentiation of the explainable models, we also identify two common types of explanations that all of the abovementioned models can be adapted to output explanations:
Rule-based Explanations: Rule-based explainability relies on generating a “set of formal logical rules formulating the inner logic of a given model.
Case-based Explanations: Rule-based explainability relies on providing valuable input-output pairs (both positive and negative) to provide an intuition of the inner logic of the model. Case-based explanations rely on the human ability to infer logic from these pairs.
Rule-Based vs Case-Based Learning Algorithm Comparison Example:
Let’s say our model needs to learn a recipe for how to make an apple pie. We have the recipes for blueberry pie, cheesecake, shepherd’s pie, and a plain cake recipe. While the rule-based learning approach tries to come up with a set of general rules for making all types of desserts (i.e., eager approach), the case-based learning approach generalizes the information exactly as needed to cover particular tasks. Therefore, it would look for the most similar desserts to apple pie in the available data. Then, it would try to customize with small variations on similar recipes.
Including rule-based and case-based learning systems, we have four main classes of white-box designs:
In the upcoming parts of this series, we will have hands-on examples of these methods.
In this post, we:
1 — Briefly covered the differences and similarities between XAI and NSC;
2 — Defined and compared black-box and white-box models;
3 — Approaches to make black-box models explainable (model properties, local logic, global logic);
4 — Compared Rule-based Explanations and Case-based Explanations and exemplified them.
In the next post, we will cover the libraries and technologies available in the market for explainability work. We will use some of these libraries to extract explanations from black and white-box models.
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