# Comparing Approaches for Explaining DNN-Based Facial Expression Classifications
## 2 Background and Related Work  
### Background on Explainable AI  
The field of explainable AI aims to make models more understandable for humans. This is important when we let AI models make important decisions. In terms of a classic example: we have a model in service of a bank that decides whether or not to give a person a loan. If someone is denied the loan, naturally that person would like to know why they did not get it and what they need to change in order for them to obtain it (counter-factual) based on 'right to explanation' [16]. Additionally, we have ethical concerns: e.g., does the model look at racial features? If the bank uses a very complex model, we cannot know this just by looking at the model on its own. We need more explanations to gain the insights we want.  
The term that stands at the centre of XAI is interpretability.There is little consensus on an exact definition for this. We use the same definition as [17] in terms of machine learning systems: 'the ability to explain or to present in understandable terms to a human'. The main problem with complex state-of-the-art models such as CNNs is that their interpretability level is very low. These models belong to the collection of black-box models: the internal workings are unintelligible.  
As to measurements to evaluate the quality of explanations, there are no formal methods for this yet [9; 18]. We can make a distinction between measurements that make use of human participants and those that do not [17].  
Measures that do not depend of human evaluation can be calculated automatically. One such property is _fidelity_ or _faithfulness_: how well does the explanation approximate the original model [19; 20]? This is measured in terms of accuracy, but with respect to the original model's predictions instead of the ground truth. This is an important property, since an explanation that does not approximate the model well does not tell us anything about the original model and is actually useless.  
The fidelity measure is distinct from _plausibility_: how convincing is the explanation for humans [20]? These two should not be mixed up, as they represent different things and should be calculated in different manners. Plausibility should be measured based on human evaluation, whereas fidelity should not. Both the fidelity and plausibility measures will used in this study.  
Achieving interpretability can be done via two distinct paths: explaining an existing, black-box model (post-hoc explaining) or designing models that are inherently interpretable [6; 21]. In this study, we look at post-hoc explanations that are thus generated after the models have been constructed. Note that there are people who favour designing inherently interpretable models rather than post-hoc explanations, particularly in high-stakes applications [5].  
Furthermore, there is a distinction between model-specific and model-agnostic explanation methods [18]. Model-specific explanations are those that only work for a specific type of model, e.g., only for CNNs, whereas model-agnostic methods work for any type of model. The latter approach treats the model essentially as a black-box and does need to look at any of the internal workings.  
The last distinction is between local and global explanations [18]. Global explanations attempt to explain the whole model, whereas local explanations explain a subset of the data or even a single data point. Multiple local explanations can also be used to approximate a global explanation. In this study we solely make use of local explanations on single data instances. Next, we discuss the two methods that are used as a basis to generate explanations in this study.