An algorithm trains a model that produces the predictions. Each step can be evaluated in terms of transparency or interpretability.
How does the algorithm create the model?
Algorithm transparency is about how the algorithm learns a model from the data and what kind of relationships it can learn. If you use convolutional neural networks to classify images, you can explain that the algorithm learns edge detectors and filters on the lowest layers. This is an understanding of how the algorithm works, but not for the specific model that is learned in the end, and not for how individual predictions are made. Algorithm transparency only requires knowledge of the algorithm and not of the data or learned model. This book focuses on model interpretability and not algorithm transparency. Algorithms such as the least squares method for linear models are well studied and understood. They are characterized by a high transparency. Deep learning approaches (pushing a gradient through a network with millions of weights) are less well understood and the inner workings are the focus of ongoing research. They are considered less transparent.
How does the trained model make predictions?
You could describe a model as interpretable if you can comprehend the entire model at once (Lipton 20168). To explain the global model output, you need the trained model, knowledge of the algorithm and the data. This level of interpretability is about understanding how the model makes decisions, based on a holistic view of its features and each of the learned components such as weights, other parameters, and structures. Which features are important and what kind of interactions between them take place? Global model interpretability helps to understand the distribution of your target outcome based on the features. Global model interpretability is very difficult to achieve in practice. Any model that exceeds a handful of parameters or weights is unlikely to fit into the short-term memory of the average human. I argue that you cannot really imagine a linear model with 5 features, because it would mean drawing the estimated hyperplane mentally in a 5-dimensional space. Any feature space with more than 3 dimensions is simply inconceivable for humans. Usually, when people try to comprehend a model, they consider only parts of it, such as the weights in linear models.
How do parts of the model affect predictions?
A Naive Bayes model with many hundreds of features would be too big for me and you to keep in our working memory. And even if we manage to memorize all the weights, we would not be able to quickly make predictions for new data points. In addition, you need to have the joint distribution of all features in your head to estimate the importance of each feature and how the features affect the predictions on average. An impossible task. But you can easily understand a single weight. While global model interpretability is usually out of reach, there is a good chance of understanding at least some models on a modular level. Not all models are interpretable at a parameter level. For linear models, the interpretable parts are the weights, for trees it would be the splits (selected features plus cut-off points) and leaf node predictions. Linear models, for example, look like as if they could be perfectly interpreted on a modular level, but the interpretation of a single weight is interlocked with all other weights. The interpretation of a single weight always comes with the footnote that the other input features remain at the same value, which is not the case with many real applications. A linear model that predicts the value of a house, that takes into account both the size of the house and the number of rooms, can have a negative weight for the room feature. It can happen because there is already the highly correlated house size feature. In a market where people prefer larger rooms, a house with fewer rooms could be worth more than a house with more rooms if both have the same size. The weights only make sense in the context of the other features in the model. But the weights in a linear model can still be interpreted better than the weights of a deep neural network.
Why did the model make a certain prediction for an instance?
You can zoom in on a single instance and examine what the model predicts for this input, and explain why. If you look at an individual prediction, the behavior of the otherwise complex model might behave more pleasantly. Locally, the prediction might only depend linearly or monotonically on some features, rather than having a complex dependence on them. For example, the value of a house may depend nonlinearly on its size. But if you are looking at only one particular 100 square meters house, there is a possibility that for that data subset, your model prediction depends linearly on the size. You can find this out by simulating how the predicted price changes when you increase or decrease the size by 10 square meters. Local explanations can therefore be more accurate than global explanations. This book presents methods that can make individual predictions more interpretable in the section on model-agnostic methods.
Why did the model make specific predictions for a group of instances?
Model predictions for multiple instances can be explained either with global model interpretation methods (on a modular level) or with explanations of individual instances. The global methods can be applied by taking the group of instances, treating them as if the group were the complete dataset, and using the global methods with this subset. The individual explanation methods can be used on each instance and then listed or aggregated for the entire group.
Lipton, Zachary C. “The mythos of model interpretability.” arXiv preprint arXiv:1606.03490, (2016).↩︎