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Title: TCAV: Relative concept importance testing with Linear Concept Activation Vectors

Paper content:
Despite neural network’s high performance, the lack of interpretability has been the main bottleneck for its safe usage in practice.
In domains with high stakes (e.g., medical diagnosis), gaining insights into the network is critical for gaining trust and being adopted.
One of the ways to improve interpretability of a NN is to explain the importance of a particular concept (e.g., gender) in prediction.
This is useful for explaining reasoning behind the networks’ predictions, and for revealing any biases the network may have.
This work aims to provide quantitative answers to \textit{the relative importance of concepts of interest} via concept activation vectors (CAV).
In particular, this framework enables non-machine learning experts to express concepts of interests and  test hypotheses using examples (e.g., a set of pictures that illustrate  the concept).
We show that CAV can be learned given a relatively small set of examples.
Testing with CAV, for example, can answer whether a particular concept (e.g., gender) is more important in predicting a given class (e.g., doctor) than other set of concepts.
Interpreting with CAV does not require any retraining or modification of the network.
We show that many levels of meaningful concepts are learned (e.g., color, texture, objects, a person’s occupation), and we present CAV’s \textit{empirical deepdream} — where we maximize an activation using a set of example pictures.
We show how various insights can be gained from the relative importance testing with CAV.
Neural networks (NNs) are capable of impressively good performance, yet understanding and interpreting their behavior remains a significant challenge.
Solving this challenge is an important problem for several reasons.
For example, explaining a system's behavior may be necessary to establish acceptability and see adoption for critical applications, such as those in the medical domain.
For scientists and engineers, any greater understanding of how neural networks function is appreciated, since it may lead to better models and help with debugging (30; 19) .Recent
work suggests that linear combinations of neurons may encode meaningful, insightful information (2; 19; 27) . However
, we lack methods to 1) identify which linear combinations (if any) relate to a given concept, and 2) how these can aid in our quantitative understanding of concepts and classification decisions. For example
, we may hypothesize that an image model that successfully classifies zebras may naturally encode concepts for 'stripe' and 'animal', somewhere in its internal representations, using a linear combination of neurons. How can we
formalize this notion, and test such a hypothesis?Neural networks
build internal representations that are far richer than the input features or output classes explicit in their training data. Unfortunately,
many machine learning interpretation methods provide results only in terms of input features. For example, the
learned coefficients in linear classifiers or logistic regression can be interpreted as each feature's classification importance. Similar first-order
importance measures for neural networks often use first derivatives as a proxy for input feature importance, as is done for pixel importance in saliency maps (8; 22) .It is critical that
model understanding and interpretation not be limited to only the concepts explicit in training data. This can be seen by
considering classification fairness-an increasingly relevant, difficult problem where interpretability can be useful-and noting that no input features may identify discriminated-against groups. For example, the Inception
model BID24 has an output class for 'doctor' but no input features identifying the concepts of 'man' or 'woman' in a way that would allow existing interpretability approaches to quantify gender bias in classification.This work introduces the method of concept activation vectors (CAV) for the following purposes. First, CAV can be used to
identify linear combinations of neurons in a layer of a model that correspond to given semantic concepts, even for new, user-provided concepts not explicit in the model's training data. Second, CAV provides quantitative
measures of the relative importance of userprovided concepts, which allows for hypothesis testing of the relationship between given concepts and the model's predictions.Testing with CAV (TCAV) is designed with the following desiderata in mind.1. accessibility: Requires little to
no user expertise in machine learning. 2. customization: Adapt to any concept
of interest (e.g., gender) on the fly without pre-listing a set of concepts before training. 3. plug-in readiness: Work without retraining
or modifying the model. BID2 . quantification: Provide quantitative explanation
that are
tied to human-relatable concept, and not input features.One of key ideas for TCAV is that we can test the relative importance between small set of concepts, rather than ranking the importance of all possible features/concepts. For example, we can gain insights by testing whether the
concept of gender was used more than the 'wearing scrubs' concept for the classification of doctor. We can also test whether or not a given concept was relevant
to the classification of a certain class. Similar forms of sparsity (i.e., only considering a few concepts
at a time) are used in many existing interpretable models (12; 7; 28; 31; 29; 4) . Note that interpretability does not mean understanding the entire
network's behavior on every feature/concept of the input BID4 . Such a goal may not be achievable, particularly for ML models with
super-human performance BID21 .TCAV satisfies these desiderata-accessibility, customization, plug-in
readiness and quantification -it enables quantitative relative importance testing for non-ML experts, for user-provided concepts without retraining or modifying the network. Users express their concepts of interest using examples-a set of data
points exemplifying the concept. For example, if gender is the concept of interest, users can collect
pictures of women. The use of examples has been shown to be powerful medium for communication
between machine learning (ML) models and non-expert users (16; 12; 13) . Cognitive studies on experts also support this approach (e.g., experts think
in terms of examples BID13 ).The structure of this paper is as follows: Section 2 relates this work to existing
interpretability methods. Section 3 explains the details of the TCAV method. In Section 4, we show 1) how this
framework can be used to identify semantically meaningful
directions in a layer and 2) the relative importance testing results that measure the relevance of concepts of interest to the classification output by the network.
We have introduced the notion of a "concept activation vector," or CAV, which is a flexible way to probe the internal representation of a concept in a classification network.
Since CAVs may be defined via a set of example inputs, rather than custom coding, they are well suited to use by non-experts.
We then described a technique (Testing with CAVs, or TCAV) for quantifying the relation between a CAV and a particular class.
The TCAV technique allows us to provide quantitative answers to questions such as, "How important are the stripes to the classification of a zebra?"To provide evidence for the value of the TCAV technique, we described a series of experiments which supported common-sense intuition, for example, that stripes are indeed important to the identification of zebras.
In addition, we used the DeepDream technique to create images whose internal representations approximate certain CAVs.
The resulting pictures were strongly evocative of the original concepts.
Finally, we described how the TCAV technique may be used to find associations between concepts, both obvious ("yellow" and "taxi") and non-obvious ("red" and "cucumber").In
addition to analyzing a single network, TCAV can be also used to compare and contrast a pair of networks. For
example, one can compare the relative importance of concepts to determine how the different choices of training process or architecture influences learning of each concept. Based
on the results, users can perform model selection based on the concepts that are more or less important for the task.An interesting direction for future work may be to explore applications of using CAVs to adjust the results of a network during inference time. Adding
a scalar multiple of a CAV to the activations of an intermediate layer can, as shown in our experiments, allow us to deemphasize or enhance conceptual aspects of an input. One potential
application, for example, might be to reduce bias the network has learned from training data.

assistant: text: This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate  the concept). We show that CAV can be learned given a relatively small set of examples. Hypothesis testing with CAV can answer whether a particular concept (e.g., gender) is more important in predicting a given class (e.g., doctor) than other sets of concepts. Interpreting networks with CAV does not require any retraining or modification of the network.