Thomas Fel
email: thomasfel@brown.edu

Julien Colin
email: juliencolin@brown.edu

Rémi Cadène
email: remicadene@brown.edu

Thomas Serre

What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods

A multitude of explainability methods and theoretical evaluation scores have been proposed. However, it is not yet known: (1) how useful these methods are in real-world scenarios and (2) how well theoretical measures predict the usefulness of these methods for practical use by a human. To fill this gap, we conducted human psychophysics experiments at scale to evaluate the ability of human participants (n = 1, 150) to leverage representative attribution methods to learn to predict the decision of different image classifiers. Our results demonstrate that theoretical measures used to score explainability methods poorly reflect the practical usefulness of individual attribution methods in real-world scenarios. Furthermore, the degree to which individual attribution methods helped human participants predict classifiers' decisions varied widely across categorization tasks and datasets.

Overall, our results highlight fundamental challenges for the field -suggesting a critical need to develop better explainability methods and to deploy human-centered evaluation approaches. We will make the code of our framework available to ease the systematic evaluation of novel explainability methods.

Introduction

There is now broad consensus that simply evaluating the test accuracy of computer vision systems does not provide a sufficient guarantee that these systems are safe to be deployed in the real-world [34,[START_REF] Serre | Deep learning: The good, the bad, and the ugly[END_REF] as those systems are capable of exploiting dataset biases and other statistical shortcuts to achieve unprecedented levels of accuracy [START_REF] Alexander D'amour | Underspecification presents challenges for credibility in modern machine learning[END_REF][START_REF] Geirhos | Shortcut learning in deep neural networks[END_REF]. A growing body of research thus focuses on making modern computer vision systems more trustworthy -in part via the * Equal contribution † Work done before April 2021 and joining Tesla Figure 1. We study the relationship between the theoretical scores used to evaluate explainability methods versus their usefulness in practical cases. A motivating example is proposed here showing three commonly used attribution methods ranked from most faithful (Saliency) to least faithful (Grad-CAM) according to the commonly used Deletion metric [START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF]. The image on the left is correctly classified by the classifier as a "Red Fox". Surprisingly, the most faithful explanation (Saliency) seems more complex to grasp for a human observer and is likely to be of little use in real-life situations. Here we conducted large-scale psychophysical experiments to evaluate the practical utility of these explanations and contrast the results with those of commonly used metrics. development of explainability methods to help interpret their predictions [START_REF] Linsley | Learning what and where to attend[END_REF][START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF]39,[START_REF] Ramprasaath | Gradcam: Visual explanations from deep networks via gradientbased localization[END_REF][START_REF] Smilkov | Smoothgrad: removing noise by adding noise[END_REF][START_REF] Sundararajan | Axiomatic attribution for deep networks[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF]. Such explainability methods will find broad societal uses -like easing the debugging of self-driving vehicles [START_REF] Zablocki | Explainability of vision-based autonomous driving systems: Review and challenges[END_REF] and helping to fulfill the "right to explanation" that European laws guarantee to its citizens [START_REF] Goodman | European union regulations on algorithmic decision-making and a "right to explanation[END_REF].

Here, we consider the problem of evaluating state-ofthe-art explainability methods. Arguably, these methods should be evaluated directly according to their usefulness to a human experimenter, i.e., how much they help understand how a system classifies arbitrary inputs. However, in practice, these methods are instead evaluated using surrogate measures defined axiomatically [6,9,[START_REF] Lipton | The mythos of model interpretability[END_REF][START_REF] Zablocki | Explainability of vision-based autonomous driving systems: Review and challenges[END_REF]. Building on previous work [START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF][START_REF] Kim | Examples are not enough, learn to criticize! criticism for interpretability[END_REF], we propose a practical and actionable definition of an explainable model which we use to introduce a human-centered evaluation framework.

A Meta-predictor perspective. Before providing a rigorous definition of interpretability, let us motivate our approach with an example: a linear classifier is often considered to be readily interpretable by a trained user because its inner working is sufficiently intuitive enough that it can be comprehended by a human user. A user can in turn build a mental model of the classifier such that it can potentially successfully predict the classifier's output for arbitrary inputs (i.e., by visually matching the degree to which "important" features associated to high classification weights are present in the input). In essence, we suggest that the model is interpretable because the output can be predicted. This concept of predicting the classifier's output is central to our approach and we conceptualize the human user as a Meta-predictor of the machine learning model. This notion of Meta-predictor is also closely related to the notion of simulatability [START_REF] Doshi | Towards a rigorous science of interpretable machine learning[END_REF][START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF]23,[START_REF] Kim | Examples are not enough, learn to criticize! criticism for interpretability[END_REF][START_REF] Pruthi | Evaluating explanations: How much do explanations from the teacher aid students?[END_REF]. We will define the term more formally in Sec. 3.1.

In contrast to linear classifiers, deep neural network models are highly non-linear and possess numerous parameters which render them hardly interpretable to human users. To make these models more interpretable, many different explainability methods have been introduced [START_REF] Chattopadhay | Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks[END_REF][START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF][START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF]39,[START_REF] Ramprasaath | Gradcam: Visual explanations from deep networks via gradientbased localization[END_REF][START_REF] Simonyan | Deep inside convolutional networks: Visualising image classification models and saliency maps[END_REF][START_REF] Tobias Springenberg | Striving for simplicity: The all convolutional net[END_REF][START_REF] Sundararajan | Axiomatic attribution for deep networks[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF][START_REF] Matthew | Visualizing and understanding convolutional networks[END_REF]. In vision, these methods typically render an explanation by calculating an importance score for each input pixel which are then displayed as a heatmap -also called an attribution map -revealing regions of the image that support the model's decision. Despite the existence of a wide range of explainability methods, assessing the quality and reliability of these methods remains an open problem. The community has taken up the issue and proposed two commonly used approaches to evaluate methods: one based on ground truth annotations [START_REF] Greenside | Learning important features through propagating activation differences[END_REF]19,[START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF][START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF][START_REF] Ramprasaath | Gradcam: Visual explanations from deep networks via gradientbased localization[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF][START_REF] Zhang | Top-down neural attention by excitation backprop[END_REF] and a second more commonly used on faithfulness metrics [19,[START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF][START_REF] Kapishnikov | Xrai: Better attributions through regions[END_REF][START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF][START_REF] Samek | Evaluating the visualization of what a deep neural network has learned[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF].

In line with previous approaches [23,[START_REF] Nguyen | Comparing automatic and human evaluation of local explanations for text classification[END_REF]39,[START_REF] Shen | How useful are the machinegenerated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels[END_REF], we put humans at the center of the evaluation procedure. More specifically, we focus on measuring how useful explanations from common methods are to human users in the sense that they can be leveraged to build a more accurate metapredictor of the model to be explained. We ran a series of large-scale online psychophysics experiments to evaluate the usefulness of 6 representative attribution methods for several binary categorization tasks derived from 3 different datasets (a standard biased dataset, a fine-grained dataset of leaf images, and ImageNet). We evaluated the ability of human participants (n = 1, 150) to use attribution maps to learn to predict the decision of different image classifiers. Our results demonstrate that (1) the degree to which attribution methods were helpful to human observers varied widely across categorization tasks; (2) the faithfulness [START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF][START_REF] Samek | Evaluating the visualization of what a deep neural network has learned[END_REF] and our usefulness measures appeared to exhibit only a very weak correlation. These results suggest that faithfulness measures are surprisingly poor predictors of the practical usefulness of current attribution methods. In the next section, we highlight the originality of our proposed human-centered evaluation framework compared to related approaches. Then, we formalize our usefulness measure and describe our experimental design with its control experiments. Finally, we report our main experiments on 3 datasets including Ima-geNet and discuss how our results suggest a critical need for better explainability methods and better quality measures for these methods.

Related work

Evaluations based on ground-truth annotations A first class of evaluation approaches scores explainability methods according to their ability to identify image locations that overlap with the target object defined either by a humanderived bounding box or a segmentation mask [START_REF] Greenside | Learning important features through propagating activation differences[END_REF]19,[START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF][START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF][START_REF] Ramprasaath | Gradcam: Visual explanations from deep networks via gradientbased localization[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF]. More recently, the evaluation method called Pointing Game [START_REF] Zhang | Top-down neural attention by excitation backprop[END_REF] counts the number of times the most important region according to the explanation intersects with the location of the object to classify.

All the aforementioned evaluation methods suffer from a critical flaw. They assume that image classifiers rely on visual features that belong to the target object. This is obviously not the case as they often rely on contextual features and all kinds of other visual shortcuts [START_REF] Geirhos | Shortcut learning in deep neural networks[END_REF]. Indeed, for a model not using the target object but shortcuts for its decision, a proper explainability method which points to this shortcut will be unfairly penalized (for not having pointed to the mask of the object). Conversely, our approach does not make any such assumptions. Irrespective of whether image classifiers provide correct or erroneous predictions, our approach assesses whether explanations ultimately help (or not) a user better understand the basis for classifying images.

Evaluations based on faithfulness measures A second class of approaches has recently started to emerge and do not make use of any ground-truth annotations -specifically to avoid some of the aforementioned limitations. They introduce measures of faithfulness to the model. Common approaches [START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF][START_REF] Samek | Evaluating the visualization of what a deep neural network has learned[END_REF] measure the change in the classification score when the most important pixels are progressively removed. The bigger the drop, the more faithful the explanation method is. To ensure that the drop in score does not come from a change in distribution of the perturbated images, the ROAR [25] methods include an additional step where the image classifier is re-trained in between each removal step. These methods do not require ground-truth annotations and hence can be used even for datasets that do not include object masks or bounding boxes. As a consequence, they are quite popular in computer vision [19,[START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF][START_REF] Kapishnikov | Xrai: Better attributions through regions[END_REF][START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF][START_REF] Samek | Evaluating the visualization of what a deep neural network has learned[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF] and natural language processing [START_REF] Arras | what is relevant in a text document?": An interpretable machine learning approach[END_REF]5,[START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF].

Nevertheless, faithfulness measures have recently been criticized as they all rely on a baseline to remove important areas, a baseline that will obviously give better scores to methods relying internally on the same baseline. [26]. More importantly, they do not consider humans at any time in the evaluation. As a result, it is not clear if the most faithful explanation methods can be practically useful to humans.

Evaluations based on human data A third class of approaches consists in evaluating the ability of humans to leverage explanations for different purposes [START_REF] Alufaisan | Does explainable artificial intelligence improve human decision-making[END_REF][START_REF] Biessmann | Quality metrics for transparent machine learning with and without humans in the loop are not correlated[END_REF][START_REF] Chandrasekaran | Do explanations make vqa models more predictable to a human?[END_REF]23,[START_REF] Mac Aodha | Teaching categories to human learners with visual explanations[END_REF][START_REF] Nguyen | Comparing automatic and human evaluation of local explanations for text classification[END_REF][START_REF] Nguyen | The effectiveness of feature attribution methods and its correlation with automatic evaluation scores[END_REF]39,[START_REF] Ramprasaath | Gradcam: Visual explanations from deep networks via gradientbased localization[END_REF][START_REF] Shen | How useful are the machinegenerated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels[END_REF]. For instance, in [START_REF] Ramprasaath | Gradcam: Visual explanations from deep networks via gradientbased localization[END_REF] and [39], explanations are evaluated according to how much they help human participants' to identify biases in models. In [39], a classifier was trained on a biased dataset of wolves and huskies: the model consistently uses the background to classify. They asked participants if they would trust the model a first time after showing images and predictions without explanations, and a second time after showing the explanations on the same images. Showing explanations had an effect on the reported trust. We also report results on this dataset. However, our psychophysical experiments differ greatly from those presented in [39] in that we do not ask users if they feel a sentiment of trust, but directly measure their ability to predict the output of the model in the absence of explanations on unseen test images. We also introduce several controls with noisy explanations.

Another line of closely related work [23,[START_REF] Nguyen | Comparing automatic and human evaluation of local explanations for text classification[END_REF][START_REF] Shen | How useful are the machinegenerated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels[END_REF] centers around the notion of simulatability which was first introduced in [START_REF] Kim | Examples are not enough, learn to criticize! criticism for interpretability[END_REF] and later refined in [START_REF] Doshi | Towards a rigorous science of interpretable machine learning[END_REF]. They introduce different experimental procedures to measure if humans can learn from the explanations how to copy the model prediction on unseen images. Some provide the explanations at test time [START_REF] Nguyen | Comparing automatic and human evaluation of local explanations for text classification[END_REF][START_REF] Shen | How useful are the machinegenerated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels[END_REF]. Similar to ours but for tabular data, [23] proposes a more difficult procedure where the explanations are hidden at test time. This forces the participants to learn the mechanisms to simulate the model at training time where the explanations are shown. Differently to ours, they also provide the groundtruth class associated with the input image during training. We argue that it should be removed since the end goal is to predict the model outputs and not to identify when the model gets an accurate prediction. A second key difference is that they have two phases with each time a training and testing time. They measure the difference between the first phase where the participants do not see any explanations and the second phase with the same examples where explanations are shown at training time. We argue that any improvement observed during the experimental condition could be the result of a learning effect because participants saw the same samples and the associated class label twice. Instead, we only have a single phase and we measure the impact of explanations by comparing results of participants that had explanations at training time with others that did not. We made sure that our results are statistically significant by having enough participants in our study.

A Meta-predictor evaluation framework

Formalism

We consider a standard supervised learning setting where f is a black-box predictor that maps an input x ∈ X (e.g., an image) to an output f (x) ∈ Y (e.g., a class label). One of the main goals of explainable AI is to yield useful rules to understand the inner-working of a model f such that it is possible to infer its behavior on unseen data points. To this end, a current approach consists in studying explanations (attribution map, concept vectors, feature visualization) for several predictions. Formally, Φ is any explanation functional which, given a predictor f and a point x, provides an information Φ(f , x) about the prediction of the predictor. In our experiments, Φ is an attribution method.

Understandability-Completeness trade-off. Different attribution methods will typically produce different heatmaps -potentially highlighting different image regions and/or presenting the same information in a different format. The quality of an explanation can thus be affected by two factors: faithfulness of the explanation (i.e., how much pixels or input dimensions deemed important effectively drive the classifier's prediction) and the understandability of the explanation for a user.

At one extreme, an explanation can be entirely faithful and provide all the information necessary to predict how a classifier will assign a class label to an arbitrary image (by rendering all the parameters of the classifiers). However, such information will obviously be overly complex to be understood by a user and hence it is not understandable. It should be noted that when this is understandable, for a linear model, for example, it is generally the most suitable explanation. Conversely, a simple explanation will be understandable but it may ultimately mislead a user if it is not faithful. That is to say, just because a human agrees with an explanation does not necessarily mean that it reflects how the model is working.

In the end, this means that there is a trade-off between the amount of information provided by an explanation and its comprehensibility to humans. In the middle of this trade-off lies the most useful explanation.

Focus on usefulness. We describe a new human-centered measure that incorporates this trade-off into a single usefulness measure by empirically evaluating the ability of human participants to learn to "predict the predictor", i.e., to be an accurate Meta-predictor. Indeed, if an explanation allows users to infer precise rules for the functioning of the predictor on past data, the correct application of these same rules should allow the user to correctly anticipate the model's Figure 2. We describe a human-centered framework to evaluate explainability methods borrowing from the concept of Meta-predictor. The framework requires a black box model f , an explanation method Φ and a human subject ψ which will try to predict the predictor, hence, the name Meta-predictor. The first step is the learning phase where the Meta-predictor is training using K samples x, together with the associated model predictions f (x) and explanations Φ(f , x). The goal of this learning phase is for the Meta-predictor to try to uncover a rule describing the functioning of the model from the triplets (x, Φ(f , x), f (x)). Then, the second step is the evaluation phase where we test the Meta-predictor accuracy on new samples x by comparing its predictions ψ(x) to those of f (x). The better his predictions are, the more useful the rules learned during his training are and therefore the method is relevant. decisions on future data. Scrutable but inaccurate explanations will result in an inaccurate Meta-predictor -just like accurate inscrutable ones. This Meta-predictor framework avoids current pitfalls such as confirmation bias -just because a user likes the explanation does not mean they will be a better Meta-predictor -or prediction leakage on the explanation -in simulatability experiments, as the explanation is available during the test phase, any explanation that leak the prediction would have a perfect score, without giving us any additional information about the model-. We will now formally describe the metric build using this framework.

We assume a dataset

D = {(x i , f (x i ), Φ(f , x i )} K i=1
used to train human participants to learn to predict a classifier's output f from K samples made of an input image x i , the associated predictions f (x i ) and explanations Φ(f , x i ).

We denote ψ (K) a human Meta-predictor after being trained on the dataset D (see Fig. 2) using explanations. We denote ψ (0) human meta-predictions after participants were trained on the same dataset but without explanations to offer baseline accuracy scores.

We can now define the usefulness of an explainability method Φ after training participants on K samples through the accuracy score of the Meta-predictor normalized by the baseline Meta-predictor accuracy:

Utility-K = P(ψ (K) (x) = f (x)) P(ψ (0) (x) = f (x)) (1)
Utility-K score thus measures the improvement in accuracy that the explanation has brought. It is important to emphasize that this Utility measure only depends on the classifier prediction and not on the ground-truth label as recommended by [START_REF] Jacovi | Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness[END_REF]. After fixing the number of training samples K, we compare the normalized accuracy of different Meta-predictor. The Meta-predictor with the highest score is then the one whose explanations were the most useful as measures compared to a no-explanation baseline.

Utility metric. In practice, we propose to vary the number of observations K ∈ {K 0 , ..., K n } and to consider an aggregated Utility score by computing the area under the curve (AUC) of the Utility-K. The higher the AUC the better the corresponding explanation method is. Formally, given a curve represented by a set of n points C = {(K 0 , Utility-K 0 ), ..., (K n , Utility-K n )} where K i-1 < K i we define our main metric Utility = AU C(C).

Experimental design

We first describe how participants were enrolled in the study, the exclusion criteria used to filter out uncooperative online participants, and our general experimental design (including both experimental and control conditions).

Participants Behavioral accuracy data were gathered from n = 1, 150 participants using the Amazon Mechanical Turk (AMT) platform (www.mturk.com). AMT is a powerful tool that allows the recruitment of massive trials of anonymous workers screened with a variety of criteria [START_REF] Matthew | Evaluating amazon's mechanical turk as a tool for experimental behavioral research[END_REF]. All participants provided informed consent electronically and were compensated $1.4 for their time (∼ 5-8 min). The protocol was approved by the University IRB and was carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki.

For each of the three tested datasets, we make sure -after filtering-to have the results from at least n = 240 participants (30 per condition, 8 conditions) in order to obtain a statistical power * of 80% to detect a medium effect size with our experimental design.

Pruning out uncooperative participants To prune out uncooperative participants, we subjected them to a 3-stage screening process. First, participants completed a short practice session to make sure they understood the task and how to use the attribution methods to infer the rules used by the model. Second, as done in [START_REF] Downs | Are your participants gaming the system? screening mechanical turk workers[END_REF], we asked participants to answer a few questions regarding the instructions provided to make sure they actually read and understood them. Third, during the main experiment, we took advantage of the reservoir to introduce a catch trial. The reservoir is the place where we store the training example of the current session, which can be accessed during the testing phase. We added a trial in the testing phase of each session where the input image corresponded to one of the training samples used in the current session: since the answer is still on the screen (or a scroll away) we expect participants to be correct on these catch trials. If they answered incorrectly, the participants were excluded from further analysis. Additional details regarding the selection criteria are given in the Appendix.

General study design It included 3 conditions: an experimental condition where an explanation is provided to human participants during their training phase (see Fig. 2), a baseline condition where no explanation was provided to the human participants, and a control condition where a bottomup saliency map [START_REF] Itti | Models of bottom-up attention and saliency[END_REF] was provided as a non-informative explanation. This last condition provides a control for the possibility that providing explanations along with training images simply increases participants' engagement in the task. As we will show in Sec. 4, such non-informative explanation actually led to a decrease in participants' ability to predict the classifier's decisions.

Each participant was only tested on a single condition to avoid possible experimental confounds (including learning confounds introduced in [23] by testing the same participants on the same images with and without explanations). The main experiment was divided into 3 training sessions (with 5 training samples in each) each followed by a brief test. In each individual trial, an image was presented with the associated prediction of the model, either alone for the baseline condition or together with an explanation for the experimental and control condition. After a brief training phase (5 samples), participants' ability to predict the classifier's output was evaluated on 7 new samples during a test phase. During the test phase, no explanation was provided to participants to assess their understanding of the classifiers while limiting confounds. One particularly problematic confound could arise from the presentation of the explanation in case the explanation leaks information about the class label. † We also propose to use a reservoir that subjects can refer † Imagine an attribution method that would solely encode the classifiers' prediction. Participants would be able to guess the classifier's prediction perfectly from the explanation but the explanation per se would not help participants understand how the classifiers work.

to during the testing phase to minimize memory load as a confounding factor which was reported in [23](see Appendix for an illustration).

Controlling for prior class knowledge

To control for users' own semantic knowledge, we balanced the samples shown to participants so that the classifiers were correct 50% of the time and incorrect 50% of the time. In this way, a participant who tries to simply predict the true class label of an image as opposed to learning to predict the model's outputs would only be correct 50% of the time. This allows us to control for a semantic confound in our experiments and avoid having participants receive a high score because they simply guess the real label.

Experiments

We performed three distinct experiments in total -using a variety of neural network architectures and 6 representative attributions methods. Each of these experiments aimed at testing the usefulness of the explanation in a different context or for a different purpose.

Setup. One of the first uses of attribution methods was for bias detection and [39] were the first to build an evaluation around the usefulness of explanation for humans to detect biases. The already existing positive results make it a good control experiment to measure the effectiveness of the framework proposed in Sec. 3.1. For this experiment, we used the same model as in the original paper: InceptionV1 [START_REF] Szegedy | Going deeper with convolutions[END_REF], and a similar dataset of Husky and Wolf to bias the model. In this situation where prior knowledge of subjects can affect their score, we balance data by showing 50% of correct prediction and 50% of incorrect prediction. A subject relying only on their prior knowledge will therefore end up as a bad Metapredictorof the model. For this experiment, the results come from 242 subjects who all passed our screening process.

As the first dataset was artificially designed, our second experiment is a real dataset with practical application proposed by [START_REF] Wilf | Computer vision cracks the leaf code[END_REF]. This botany dataset contains over 5, 000 images divided into 19 classes. The predictor used is a VGG-16 [START_REF] Simonyan | Deep inside convolutional networks: Visualising image classification models and saliency maps[END_REF]. We then selected 2 classes that could not be classified using shape in order to avoid that subjects solve the task too easily and force them to rely on non-trivial features provided by the explanations (veins, leaf margin...). Let us note that this scenario is far from being artificial and is a genuine problem for the paleobotanist [START_REF] Wilf | Computer vision cracks the leaf code[END_REF]. The classes Betulaceae and Celastraceae fulfill this condition and have been chosen. As subjects are lay participants from Amazon Mechanical Turk we do not expect them to be experts in botany, therefore we do not control for prior knowledge. In this experiment, 240 subjects have passed all our screening processes.

Finally, for our third experiment we used ImageNet [START_REF] Deng | ImageNet: A Large-Scale Hierarchical Image Database[END_REF], a very large image dataset widely used in computer vision In our Meta-predictor framework, we suggest not to show the actual label during training since the goal is to predict the model. Moreover, during the test phase (on images not seen at the training time) the explanation is not provided since we aim to measure how well a user is able to predict the model and not the ability of the explanation to leak the label. and the field of Explainable AI is no exception [START_REF] Elliott | Explaining classifiers using adversarial perturbations on the perceptual ball[END_REF][START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF]25,[START_REF] Nguyen | The effectiveness of feature attribution methods and its correlation with automatic evaluation scores[END_REF][START_REF] Shen | How useful are the machinegenerated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF]. We use this dataset because we expect it to be representative of real-world scenarios where it is difficult to understand what the model is relying on for its decisions. Moreover, previous works have pointed out that attribution methods were not useful on this dataset [START_REF] Shen | How useful are the machinegenerated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels[END_REF], we have thus chosen to extend our analysis to this particular case. We use a ResNet50 [START_REF] He | Deep residual learning for image recognition[END_REF] pretrained on this dataset as predictor. Because prior knowledge is a major confounding factor on ImageNet, we select a pair of classes that was heavily missclassified by the model, to be able to show subjects 50% of correct prediction and 50% of incorrect prediction to control for prior knowledge: the pair Kit Fox and Red Fox fits this requirement. In this experiment, the results come from 241 subjects who all passed our screening process.

For all experiments, we compared 6 representative attribution methods: Saliency (SA) [START_REF] Simonyan | Deep inside convolutional networks: Visualising image classification models and saliency maps[END_REF], Gradient Input (GI) [START_REF] Ancona | Towards better understanding of gradient-based attribution methods for deep neural networks[END_REF], Integrated Gradients (IG) [START_REF] Sundararajan | Axiomatic attribution for deep networks[END_REF], Occlusion (OC) [START_REF] Matthew | Visualizing and understanding convolutional networks[END_REF], SmoothGrad (SG) [START_REF] Smilkov | Smoothgrad: removing noise by adding noise[END_REF] and Grad-CAM (GC) [START_REF] Ramprasaath | Gradcam: Visual explanations from deep networks via gradientbased localization[END_REF]. Further information on these methods can be found in the Appendix.

As a faithfulness evaluation, we used the Deletion [START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF] metric which is commonly used to compare attribution methods [19,[START_REF] Fong | Interpretable explanations of black boxes by meaningful perturbation[END_REF][START_REF] Kapishnikov | Xrai: Better attributions through regions[END_REF][START_REF] Vitali Petsiuk | Rise: Randomized input sampling for explanation of black-box models[END_REF][START_REF] Samek | Evaluating the visualization of what a deep neural network has learned[END_REF][START_REF] Thomas | Look at the variance! efficient black-box explanations with sobolbased sensitivity analysis[END_REF]. A low Deletion score indicates a good faithfulness, and we report the faithfulness score as 1-Deletion such that a higher faithfulness score is better.

Sanity check for Usefulness

The Utility score encodes the quality of the explanations provided by a method, the higher the score, the better the method. Fig. 4 shows the Utility-K scores for each method after different number of training samples were used to train participants for the biased dataset of Husky vs Wolf.

A first observation is that the explanations have a positive effect on the Utility-K score: the explanation allows participants to better predict the model's output (as the Util- Moreover, the difference between the scores of the methods and the baseline implies that the humans used the explanations well. This is confirmed by the fact that the explanation based on Gabors filters (Control) misled the subjects, causing them to do worse than the baseline. Grad-CAM is the method with the most positive effect and is statistically significantly better than the other methods. ity scores are above 1). This is confirmed with an Analysis of Variance (ANOVA) for which we found a significant main effect, with a medium effect size for our conditions (F (7, 234) = 4.58, p < .001, η 2 = 0.121). Moreover, the only score below the baseline is that of the control explanation (based on Gabor filters), which do not make use of the model. We further explore the effect of our conditions by performing pairwise comparisons using Tukey's Honestly Significant Difference [START_REF] Tukey | Comparing individual means in the analysis of variance[END_REF] to compare the different explanations between themselves. The users in the control condition did not perform better than the condition without explanation.

The control condition performing worse than the baseline is an important result, as it suggests that attributions methods suspected to be more image-based than model-based [START_REF] Adebayo | Sanity checks for saliency maps[END_REF] can be misleading. This finding also reinforces the idea that participants did make use of the explanations to try to understand the model, which led them to either get better at predicting when the explanations highlighted a behavior of the model, or misleading them when the explanations were not unrelated to the model's decision. Here Grad-CAM seems to be the best method as it is the only method significantly better than our baseline condition (p = .002) and the control condition (p < .001). Thus, participants who received the Grad-CAM explanations performed much better than those who did not receive them. The attributions maps, therefore, provided participants with essential information about the model that allowed them to train a more accurate Meta-predictor.

Finally, some attribution methods seem more useful than others. We found that Integrated Gradients and Saliency are the worst explanations as both of them are significantly less useful than Grad-CAM (p = .01, and p = .02 resp.). In between those 2 groups lies Integrated Gradients, Smooth-Grad and Occlusion which do not seem to have any significant difference in their usefulness to subjects.

Figure 5. Utility-K for Leaves dataset. The Utility-K score is measured on [START_REF] Wilf | Computer vision cracks the leaf code[END_REF] botany dataset. As in the previous series of experiments, the participants were able to leverage the explanations to learn to predict the model's predictions. All the conditions tested here were better than the baseline.

In Fig. 5, we show results on the Leaves dataset obtained with 240 subjects. The ANOVA across all conditions revealed a significant main effect, with a medium effect for our conditions (F (7, 232) = 3.12, p = .004, η 2 = 0.086). This implies that explanation also had an effect on the construction of a better Meta-predictor in this case. More precisely, according to Tukey's Honestly Significant Difference test we found that the best explanations are Saliency and SmoothGrad as they are the only ones to be significantly better than our baseline (WE) (p = .014 and p = .48 respectively). Concerning the ranking of the methods, none is significantly better than the others. However, the Utility score of Gradient Input seems to be worse than the other method and SmoothGrad slightly leads the ranking. A surprising result is that Saliency which was one of the worst explanation on the first use case, is now the best explanation on this use case.

These two experiments allow us to verify that attributions maps could allow humans to better predict and thus to better understand their model. We now wish to analyze the relationship between the methods being the most useful and the current regularly used faithfulness metrics. The results of our utility-based human evaluation show that not only are attribution methods useful in helping users understand their model, but that there are substantial differences among these methods. It would then be useful to be able to predict this ranking without resorting to human experiments which can be costly. The literature currently uses automatic metrics (which do not require humans) of faithfulness to compare methods between them, it seems legitimate to ask if the best method on these benchmarks is the one that helps humans the most.

Faithfulness metric as a proxy for Usefulness?

On the first dataset, where Grad-CAM is the most useful method and Integrated Gradients, Saliency the worst ones, the faithfulness ranking in Tab. 1 designates Grad-CAM as the least faithful method behind Saliency which gets the best score. Surprisingly, we can observe that these results do not seem to be only independent of the utility score but negatively correlated. Thus, the faithfulness score does not indicate how useful a method will be on the bias detection task.

On the second dataset, the best explanations are Smooth-Grad and Saliency , with Gradient Input being the worst. From a faithfulness point of view, it is the opposite (see Tab. 1) since Gradient Input appears as one of the most faithful, behind Integrated Gradients. Moreover the most useful method: Saliency is designated as the less faithfull, just before Occlusion. Overall, all the methods tested here are more faithful than the baseline.

In Fig. 6, we summarize our results on all 3 datasets by showing that explanations are useful to the subjects on the Husky versus Wolf and Leaves datasets as the Utility scores are above 1. However, our scores are not correlated with the faithfulness score. Indeed, we observe a weak correlation (ρ = 0.27) for the Leaves experiment and a strong anti-correlation for the Husky experiment (ρ = -0.75) indicating that the actual faithfulness metrics are a poor proxy of usefulness.

When explanations are Useless

Fig. 6 shows that, on the ImageNet dataset, the set of methods tested (triangle marker) does not exceed 1, the baseline accuracy. Indeed, the experiment carried out, even with an improved experimental design, led us to the same conclusion as previous works [START_REF] Shen | How useful are the machinegenerated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels[END_REF]: none of the tested attribution methods are useful. The ANOVA done on the N = 241 subjects tells us that there is no significant main effects of our conditions (F (7, 233) = 1.2, p > .05). And as expected, the follow-up Tukey's Honestly Significant Difference test showed that none of the methods tested are useful against the baselines. Therefore no ranking based on usefulness could be established. Our previous 2 experiments indicate that, when explanations are useful to humans, their faithfullness seems to be either weakly or inversely correlated. In contrast to the Utility results, the faithfulness metric gives us a clear ranking (see Tab. 1) of the methods, indicating that once again it fails to predict the cases where none of the methods is useful.

We note that the control condition -that only needs the image to be generated-is the 3rd most faithfull explanation and is very close to the leading method. This may indicate the necessity for better baselines in the faithfullness evaluation to put into perspective the results.

Discussion

In summary, the proposed experimental protocol allowed us to verify two things: (1) that attributions methods can be useful -some more than others-, and (2) that the current faithfulness score of a method poorly reflects its practical usefulness to understand a model.

Regarding [START_REF] Adebayo | Sanity checks for saliency maps[END_REF], the experiments conducted on the Husky vs Wolf dataset and on the Leaves dataset allowed us to see that our experimental design effectively captures the usefulness of explanations: humans perform better with explanation than without it. Moreover, with our control condition, we have results that highlight the practical risk of attributions methods that rely too heavily on image and not enough on model [START_REF] Adebayo | Sanity checks for saliency maps[END_REF]. This reinforces the idea that it is critically important to evaluate the usefulness of an explanation.

The second point concerns the evaluation currently used to evaluate attribution methods. These faithfulness evaluations are poor substitutes for utility. They can therefore only be considered as sanity checks, in the strict and limited sense of the term. Since these evaluations do not give an equivalence of utility, to properly evaluate attribution methods, it is necessary to correctly put the human in the loop to avoid the previous pitfalls.

Finally, as pointed out in previous works [START_REF] Shen | How useful are the machinegenerated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels[END_REF], in some cases the explanation is not useful to humans. More importantly, this weakness cannot be predicted from current metrics. One of the tracks considered to explain this dysfunction is linked to the attribution methods which are limited to describe a spatial area, thus leaving to the human the care to bring their semantics, leaving a possible ambiguity. These inherent weaknesses of attribution methods suggest that other (potentially complementary) methods of explanation should be considered.

Conclusion

In this work, we propose a new experimental design to evaluate whether explanations are useful to humans using a Meta-predictor perspective. We also perform an online experiment on 3 datasets to obtain a ranking of the most useful explanations for humans. In parallel, we evaluate these same attribution methods according to Deletion, a widely used faithfullness metric. We find that the ranking of explainability methods based on their faithfullness does not correlate with their practical usefulness, which may indicate that we are straying too far away from the original goal of explainable AI. Our results suggest a need to develop better metrics and human-centered methods. 9. Quiz. Through a quiz, we make sure that users read and understood the instructions. Participants that did not answer correctly every question on the first try were excluded from further analysis. Figure 10. Catch trial. We use a reservoir (to store all the examples of the current training session) that participants can refer to during the testing phase to minimize memory load. At the top of the screen is the reservoir, at the bottom of the screen is a trial from the testing phase. We take advantage of the reservoir to introduce a catch trial. We added a trial in the testing phase of each session where the input image corresponded to one of the training samples used in the current session: since the answer is still on the screen (or a scroll away) we expect participants to be correct on these catch trials. Participants that failed any of the 3 catch trials (one per session) were excluded from further analysis.

B. Attribution methods

B.1. Methods

In the following section, the formulation of the different methods used in the experiment is given. We define f (x) the logit score (before softmax) for the class of interest. An explanation method provides an attribution score for each input variables. Each value then corresponds to the importance of this feature for the model results.

Saliency [START_REF] Simonyan | Deep inside convolutional networks: Visualising image classification models and saliency maps[END_REF] is a visualization technique based on the gradient of a class score relative to the input, indicating in an infinitesimal neighborhood, which pixels must be modified to most affect the score of the class of interest.

Φ SA (x) = ||∇ x f (x)||
Gradient Input [7] is based on the gradient of a class score relative to the input, element-wise with the input, it was introduced to improve the sharpness of the attribution maps. A theoretical analysis conducted by [START_REF] Ancona | Towards better understanding of gradient-based attribution methods for deep neural networks[END_REF] showed that Gradient Input is equivalent to -LRP and DeepLIFT [START_REF] Shrikumar | Learning important features through propagating activation differences[END_REF] methods under certain conditions: using a baseline of zero, and with all biases to zero.

Φ GI (x) = x ||∇ x f (x)||
Integrated Gradients [START_REF] Sundararajan | Axiomatic attribution for deep networks[END_REF] consists of summing the gradient values along the path from a baseline state to the current value. The baseline is defined by the user and often chosen to be zero. This integral can be approximated with a set of m points at regular intervals between the baseline and the point of interest. In order to approximate from a finite number of steps, we use a Trapezoidal rule and not a left-Riemann summation, which allows for more accurate results and improved performance (see [START_REF] Sotoudeh | Computing linear restrictions of neural networks[END_REF] for a comparison). The final result depends on both the choice of the baseline x 0 and the number of points to estimate the integral. In the context of these experiments, we use zero as the baseline and m = 80.

Φ IG (x) = (x -x 0 ) 1 0 ∇ x f (x 0 + α(x -x 0 )))dα
SmoothGrad [START_REF] Smilkov | Smoothgrad: removing noise by adding noise[END_REF] is also a gradient-based explanation method, which, as the name suggests, averages the gradient at several points corresponding to small perturbations (drawn i.i.d from a normal distribution of standard deviation σ) around the point of interest. The smoothing effect induced by the average helps reduce the visual noise and hence improve the explanations. In practice, Smoothgrad is obtained by averaging after sampling m points. In the context of these experiments, we took m = 80 and σ = 0.2 as suggested in the original paper.

Φ SG (x) = E ε∼N (0,Iσ) (∇ x f (x + ε)) Grad-CAM [START_REF] Ramprasaath | Gradcam: Visual explanations from deep networks via gradientbased localization[END_REF] can be used on Convolutional Neural Network (CNN), it uses the gradient and the feature maps A k of the last convolution layer. More precisely, to obtain the localization map for a class, we need to compute the weights α k c associated to each of the feature map activation A k , with k the number of filters and Z the number of features in each feature map, with α c k = 1 Notice that the size of the explanation depends on the size (width, height) of the last feature map, a bilinear interpolation is performed in order to find the same dimensions as the input.

Occlusion [START_REF] Matthew | Visualizing and understanding convolutional networks[END_REF] is a sensitivity method that sweeps a patch that occludes pixels over the images, and uses the variations of the model prediction to deduce critical areas. In the context of these experiments, we took a patch size and a patch stride of of 1 tenth of the image size.

Φ OC i = f (x) -f (x [xi=0] )

B.2. Examples of explanations

Figure 3 .

 3 Figure 3. Example of experiments data. Triplets: image, explanation (Grad-CAM here), prediction shown to the participants at training time.In our Meta-predictor framework, we suggest not to show the actual label during training since the goal is to predict the model. Moreover, during the test phase (on images not seen at the training time) the explanation is not provided since we aim to measure how well a user is able to predict the model and not the ability of the explanation to leak the label.

Figure 4 .

 4 Figure 4. Utility-K for Husky vs Wolf dataset. The Utility-Kof the explanation, or the accuracy of the human Meta-predictor after training, is measured after each training session (3 in total). All methods have a positive effect on the score obtained -they improve the subjects' ability to predict the model -and are thus useful.Moreover, the difference between the scores of the methods and the baseline implies that the humans used the explanations well. This is confirmed by the fact that the explanation based on Gabors filters (Control) misled the subjects, causing them to do worse than the baseline. Grad-CAM is the method with the most positive effect and is statistically significantly better than the other methods.

Figure 6 .

 6 Figure 6. Utility vs Faithfulness correlation. The utility scores on the two datasets Husky vs. Wolf (point marker) and Leaves (square marker) are plotted showing a poor or anti-correlation between the two measures. Concerning the ImageNet dataset (triangle marker), the Utility scores are insignificant since none of the methods improves the baseline.

Figure 8 .

 8 Figure 8. Practice session. Through a practice session, which is a simplified version of the main experiment, we evaluate if users understand how to read and use explanations. Participants that failed to predict correctly any of the 5 cat test images on the first try were excluded from further analysis.

Figure

  Figure9. Quiz. Through a quiz, we make sure that users read and understood the instructions. Participants that did not answer correctly every question on the first try were excluded from further analysis.

  Examples of explanations from the different attributions methods evaluated through our experiments on the Husky vs Wolf dataset (fig 11), the Leaves dataset (fig 12) and the ImageNet dataset (fig 13).

Figure 11 .

 11 Figure 11. Examples of images from the Wolf vs Husky experiment, alongside their respective: Control explanation (which is a noninformative explanation) as well as the different Attribution methods evaluated in our experiment.

Figure 12 .

 12 Figure 12. Examples of images from the Leaves experiment, alongside their respective: Control explanation (which is a non-informative explanation) as well as the different Attribution methods evaluated in our experiment.

Figure 13 .

 13 Figure[START_REF] Alexander D'amour | Underspecification presents challenges for credibility in modern machine learning[END_REF]. Examples of images from the ImageNet experiment, alongside their respective: Control explanation (which is a non-informative explanation) as well as the different Attribution methods evaluated in our experiment.

Table 1 .

 1 Faithfulness scores. Average faithfullness score for each of the 3 datasets considered. Higher is better. The first and second best results are respectively bolded and underlined.

	Method	Husky vs Wolf Leaves ImageNet
	Control (Random)	0.14	0.29	0.83
	Saliency [45]	0.18	0.55	0.84
	Grad.-Input [7]	0.17	0.74	0.78
	Integ.-Grad. [49]	0.16	0.77	0.85
	SmoothGrad [46]	0.10	0.56	0.69
	GradCAM [41]	0.07	0.58	0.73
	Occlusion [55]	0.09	0.49	0.79

* The statistical power is the probability that we correctly reject the null hypothesis (H 0 ) if a specific alternative hypothesis (H 1 ) is true

Acknowledgments

This work was conducted as part the DEEL ‡ project. It was funded by the Artificial and Natural Intelligence Toulouse Institute (ANITI) grant #ANR19-PI3A-0004. MC was funded by ANR grant VISADEEP (ANR-20-CHIA-0022). TS was funded by ONR (N00014-19-1-2029) and NSF (IIS-1912280). The computing hardware was supported in part by NIH Office of the Director grant #S10OD025181 via the Center for Computation and Visualization.

A. Human experiments A.1. Experimental design

Figure 7 summarizes the experimental design used for our experiments. The participants that went through our experiments are users from the online platform Amazon Mechanical Turk (AMT). We prioritized users with a Master qualification (which is a qualification attributed by AMT to users who have proven to be of excellent quality) or normal users with high qualifications (number of HIT completed = 10000 and HIT accepted > 98%) . Then, participants are split into the different conditions -every participant will only go through one condition. The 3 possible conditions are: an Explanation condition where an explanation is provided to human participants during their training phase, a Baseline condition where no explanation was provided to the human participants, and a Control condition where a non-informative explanation was provided. The main experiment was divided into 3 training sessions each followed by a brief test. In each individual training trial, an image was presented with the associated prediction of the model, either alone for the baseline condition or together with an explanation for the experimental and control condition. After a brief training phase (5 samples), participants' ability to predict the classifier's output was evaluated on 7 new samples (only the image, no explanation) during a test phase. To filter out uncooperative participants we also add a catch trial (fig 10) in each test session.

A.2. 3-stage screening proccess

In this section, we show screens of our experiments, for each screening process: the practice session (fig 8), the quiz (fig 9), and the catch trials (fig 10). Participants that failed any of the 3 screening process were excluded from further analysis.


The Songs Improvised by the Poets of the Brazilian Northeast: Tradition, Urbanization,

Expansion, and Animation of a Territory

Thierry Rougier

This article is based on an ethnomusicological survey conducted in Brazil by the association CORDAE (Centre Occitan de Recherche, de Documentation et d'Animation Ethnographiques), BP 40,23 Grand rue de l'Horloge, 81170 France. Tel: (33) 05 56 62 19 17;email: talvera@talvera.org;website: www.talvera.org;director: Daniel Loddo. As a musician and an ethnologist, I teach at the University of Bordeaux. With a particular interest in Latin America, in 1992 I started to research the popular singers who improvise their songs. That took me to the land of Nordeste, the northeastern region of Brazil. There, I found amateurs involved in improvised poetry: the cantadores and their admirers, gathered in nocturnal sessions called cantorias.

The passion for the spoken word expressed in accordance with the rules of art is a cultural element shared throughout Latin America. Singing poetry and tournaments among poets are widely appreciated. But in Nordeste, people give supreme importance to the total improvisation of sung verses. That is the art of the repente (meaning "sudden"), a local tradition whose vivacity is remarkable. And today, its actors are much more numerous than ever before.

They are called repentistas, cantadores, or violeiros: repentistas, because they always renew their verses; cantadores, because they are popular artists capable of singing any subject proposed by the public; violeiros, because they play a viola, a kind of guitar emblematic of their profession.

Urbanization boosted the evolution of the repente tradition, which originally appeared in the nineteenth century in Sertão, a semi-arid rural area of inland Nordeste.

Droughts caused periodic exoduses from this region and Nordestinos constituted the main contingent of migrant workers throughout Brazil. For the last half century, they have participated in the industrialization and the construction of large cities. This rural exodus has depopulated the Sertão, whose itinerant poets of old were transformed into emigrant poets, following their public toward the cities and adapting their art to the modernity. Urbanization requires social adjustment and cultural adaptation. In the following chapter, I will explain the tradition of the repente, the art of the word, whose ethics is spontaneity and adaptation; I will then go on to show how improvising poets have conquered new production spaces, diversifying their activities; and finally, I will discuss the reason for the development of a popular expression in cities where cultural diversity and the pressure of modernity are real-life: the improvisers practice a form of animation combining creation and mediation.

Improvisation: A Mobile Tradition

The emergence of improvised verse is stimulated by the encounter between poets and admirers in a night session. A protocol rules traditional singing: two singers play the viola, sitting side by side at the foot of some wall, in front of an audience that often asks them for themes or styles. The poems are always produced by two authors alternating the verses. The poetic dialogue, its emulation, and some rules (for example the obligation to retake in the first line the rhyme that the other has left at the end of his verse, a rhyme that cannot be predicted), everything guarantees the authenticity of improvisation. It is the essential value, before any other formal criteria. In this way, the repentistas are distinguished from the other Nordeste poets, the coquistas. The latter play a pandeiro or drum and usually put together a stanza mixing memorized elements and improvised passages. In addition to this genre, known as coco de embolada, there are other genres of oral poetry that are more local, such as: the coco de roda, which combines dance, percussion, a choir, and poetry in the coastal zones; the maracatu rural, from Nazaré da Mata in the State of Pernambuco, in which improvised verses alternate with very fast music; and the aboio, linked to the breeding of cattle, whose poets sing without accompaniment, and which has some Arab or Turkish influence. Compared to these very varied genres, the repente is reputed to carry the highest degree of poetic improvisation. I will now characterize its music.

The Music

Unlike the verses, the melody that sustains them is not improvised. The singers learn many traditional tunes by ear, and they use one of these when singing, with some personal variation. Thus, the melodies evolve gradually. New tunes are also created by derivation from the old ones. The poet who opens the song chooses from this vast repertoire the air that seems most convenient to sustain the theme to be developed. The poet who responds to him must repeat this air, demonstrating a particular feeling. This is the constant base of the poem. Certain tunes are always associated with a peculiar poetic form, including a certain refrain. Sometimes, the singer outlines the chosen tune by preluding it on his viola.

The melodies are composed in order to sing a note per syllable, except the last one that can be decorated with vibrato or melisma. They are functional and designed to be sung in a metrical poetry in which the lines measure 7, 10, or 11 syllables. The minor mode is exceptional in melodies that have a strong modal character. The most frequent modes are our major scale and alterations of this (the fourth degree of the scale can be pitched a half up, or the seventh pitched a half down, or a combination of both). The notes are often not sung in a temperate way. That means there is a slight difference of tone between the sung and the corresponding note in the playing of the viola, divided into twelve equal halves as in a guitar. That is more noticeable when the singer comes from a rural area, since urbanization tends to normalize the modes. Whatever the case, the modes are very variable, but the structure remains the same: the tunes have to be a good support for the poetic forms, which also suffer constant evolution.

The viola playing is the indispensable element to improvisation: the repentistas say that their inspiration cannot do without the sound of the strings. They carefully tune the two instruments, which have between 7 and 10 metal strings, some double or triple, grouped into 5 choirs. However, with the exception of some prelude or counterpoint, the viola music is extremely monotonous. The whole session is accompanied by the strumming of fingers on a single major chord, barely adorned with any other notes. The constant character of harmony and rhythm is the unifying element; it allows the creation of the unpredictable by the improviser. At the same time, he has to memorize some of what his colleague has said, and think about what he is going to say in the next stanza, put together in a few seconds.

The instrument is the poet's tool in the performance, because its music reconciles attention and concentration.

To illustrate that, I will talk about the songs that are not improvised and that mark a pause in the protocol of the improvisation session. At some point, a listener may request a composed song, which is usually nostalgic. It will be interpreted by one of the poets who will then be accompanied with diverse harmonies, as well as major and minors chords (three to six chords for an ordinary song). In short: for a well-known song, the versatility of chords; for the suspense of the poetic tournament, the monolithic character of harmony.

The musical aspects are important for the listeners, who have their preferences according to the vocal, rhythmic, or scenic characteristics of the singer.

However, for a singer, these aspects are not criteria determining his professional quality, which depends only on his mastery of poetry and language. Regarding the language, it is the Portuguese in use in Brazil, with high demands on the richness of vocabulary and the sound quality of the rhymes. Poets sing according to the pronunciation of their region of origin and can integrate regionalisms that are often indigenous words. But I am not aware of any improviser producing poetry in any indigenous language of Brazil. Now we must talk about poetry.

The Poetry

The singers perform their oral performances following what they call modalities. That is the structure that links text to music. They have almost fifty different modalities, which are in constant evolution: some fall into disuse, new ones emerge, and they are generally invented in anonymity. The modality determines the metric, the length of the verses, the structure of the rhymes, and sometimes a refrain or a forced melody. The most used modality is the sextilha, a six-line verse of seven syllables per line, with a rhyme on the even lines. The décima is also widely used and considered a masterpiece in poetic construction.

There are two types of décima: one has ten lines of seven syllables each line, while the other has ten lines of ten. To build his verse, the improviser has to find four different rhymes and organize them according to a complex structure: ABBAACCDDC. This is what Spaniards call the décima espinela, from the name of Vicente Espinel, an Andalusian poet of the sixteenth century. It is still in use in improvised poetry by Chilean, Argentine, and Uruguayan payadores, and by Cuban trovadores. That means it is a poetic form common to Latin American countries. Here it is worth pointing out an important distinction: when in Spanish they count eight syllables in a line, in Portuguese they count seven because the syllables are counted only until the last stressed one (which is necessarily followed by an unstressed one, or a silence). Likewise, the decasilábicos lines of Brazil correspond to the lines of eleven syllables of the Spanish speakers.

The décima often ends in a mote, a refrain of one or two lines that someone proposes to the two poets before they begin to improvise. One must memorize the mote to develop its theme, reuse its rhymes in lines 6, 7, and 8, and repeat it at the end of the verse.

Another modality with the same exigency is the galope, whose peculiarity is a line of eleven syllables. It is used to sing bucolic themes. Other modalities present different demands, when the two improvisers must alternate the lines instead of the verses. For example, in a modality called quadrão perguntado, they must respond in a single line to what was asked in the previous line. In the modalities o cantador de você and gabinete, manwhile, the singers can show their virtuosity by repeating a long refrain.

The number of forms that can be heard during a cantoria session has no equivalent in other genres of oral poetry. To express themselves, poets undergo a demanding tradition that they move forward, inventing new forms, with the goal of excellence. This care for diversification, in addition to an aesthetic particularity, is a test of flexibility for the cantador-a thought capable of moving within any frame is also capable of mobilizing in any circumstance. Thus the critical spirit is trained, which is the cantador's ethical requirement.

The Topics

That ethic is revealed by listening to what is sung in the sessions. What is said before the public and how to say it is the major criterion for judging an improvisation. This is termed oração, while the other formal criteria are rima and métrica: a poet has to produce regular lines, without breaking the rhythm; he also has to find many good rhymes. In a session, the listeners propose a subject, suggesting it aloud or by writing it down on a piece of paper deposited on a tray that also collects their contribution. In the same way, a fan of poetry can propose a versified topic that will be the mote ending the stanzas. The singers are then obliged to satisfy the requests. They fulfill their role as popular artists by staging the listeners' lives. Each session is an exchange time: ideas circulate and feelings are shared.

There is a permanent dialogue between the poets, and between them and their listeners.

Thus, the topics are always renewed, through the public in the varied places that singers travel to.

The subject field is very wide. It embraces: the poetic art itself, singing about the value of poets and the grief of those who have left us; fun themes, with the most appreciated being that of a challenge among singers; love, whether happy or unhappy; bucolic subjects; existential or philosophical themes; those that we can term ethical, moralistic, or edifying; and, lastly, social and political issues. A repentista must know how to sing about any subject. That is possible by the preparation of improvisers. They refrain from using verses from memory, but learn many words to enrich their imagination. They read books, dictionaries, newspapers, and the Internet, listen to radio and watch television, and especially attend the performances of other repentistas. Of necessity, they learn about current social, political, and newsworthy events because they have to comment such matters. They are the scholars of popular culture.

Their knowledge legitimizes their satirical role. At a playful level, they can exchange insults in the cantoria, which is a theater of rivalry. From a moral point of view, they have complete freedom of speech to speak about power and denounce its abuses and imperfections, in harsh words. The listeners take advantage of that to propose the most pulsating subjects that the singers will treat with critical spirit. On several occasions I came across the opinion that, "the singers say what the people would say if they could." Many poets consider themselves defenders of the people, at the forefront of social concerns.

Many conceive of their art as a popular education movement and cantorias as a moment of emancipation of the spirits.

A Conquering Movement: New Production Spaces, New Actors

This description of the cantoria was based on a traditional encounter performed pé de parede (at the foot of a wall), because they are usually organized in a private house for some party, or in bar or restaurant so as to share the benefits between patron and singers.

However, any description would be very incomplete without mentioning new production spaces. Since 1950, contests have been organized by cultural associations in which poets and fans act to promote their art. These events take place in prestigious places of culture such as theatres, welcoming hundreds of people-a larger audience than in family, private, or country sessions. Contests are competitions between duos: two collaborating singers stand on the stage for a thirty-minute presentation, the viola slung over their shoulders, next to a jury that sets the issues and grades the duo, only according to the three criteria of rima, métrica, and oração. Facing the stage, spectators encourage their favorites. The winners win trophies, cachet, and increased fame in the repente community, which is highly competitive.

The contests are very successful, with at least one taking place every week in some Brazilian city. Thus, there are frequent opportunities to bring different titles into play. These competitions, invented by the singers, contribute to promoting their art and professionalizing them-nowadays, cachet payment tends to replace the collection on the tray, and contracts are substituted for oral commitments. Tradition evolves in this way, thanks to the rivalry between poets who invent unprecedented circumstances to improvise in.

This occurs on radio programs devoted to improvisation, produced and animated by the poets. During the broadcast, they promote upcoming events and improvise, despite the physical absence of the public, which instead sends in requests by phone or mail. In the areas most affected by the rural exodus, broadcasting is a regular occasion to listen to repentistas. Several have learned the basics of their art in this way, before frequenting their first sessions.

Since the late twentieth century and the development of tourism on the coast, beaches are a new space for more humble poets. Without any specific requests, they sing a few stanzas addressing the tourists. They are criticized by their more famous colleagues for walking away from tradition: they sing by day and do not satisfy any specific public demands.

In these circumstances, in contrast to traditional sessions, there is little interaction because on the beach they merely beg for money. But what are known as "beach poets" have introduced an unprecedented form of improvisation in new and inauspicious spaces. And in that way, these poetas de praia contribute to the expansion and visibility of their practice.

It is also worth mentioning recent festivals in public squares, which take place in a context of amplified music and eclectic programming. There, the singers have the opportunity to sing to thousands of people.

Such diversification on the part of the actors typically appears after an opening opportunity. About forty years ago, female poets emerged in an exclusively male professional environment. The evolution toward the mix of social practices in Brazil was used by these women to speak up in common. They equal their male counterparts in terms of artistic ability and critical spirit, and conform to their vocation as a popular poet commenting on any topic. But it has not been easy for these women to emancipate themselves from the reserved attitude traditionally assigned to them. That may explain the small number of active women poets today. On the other hand, the audiences are fully mixed; there are women passionate about the poetry that accompany the poets on their tours or do not miss a contest. However, during my research I have met only two female poets: they were witness to the difficulty that exists in overcoming the prejudices, and were proud of the current evolution.

Record production is an important aspect of the activity of today's singers, who use their CDs like a business card to look for contracts. The recorded material is oral work of a different nature because it is difficult to improvise in a studio, without any inspiration stimulated by requests of listeners. This is, then, not repente, but rather an elaborate form of sung poetry. Instead of writing the poems, the authors repeat them until they reach what they feel is the best expression. Topics are chosen by the constituted duo and alternate with composite songs. Like a written published poem, a CD leaves a trail, unlike the evanescence of an authentically improvised verse. Fans use this medium to hear sung poetry and compensate for the scarcity of events where they live.

As a result, it is estimated nowadays that there are thousands of these singers and that they far outnumber their predecessors. The dynamism of the tradition accompanies a remarkable qualitative evolution: most cantadores of old were illiterate, lived in precarious conditions, and cared little for musical, scenic, or corporate criteria-a fact that does not minimize in any way their quality as great poets whose memory remains alive in the tradition. The epigones work to rise socially, studying, promoting good quality shows, and organizing their profession through associations, hand in hand with other defenders of popular culture.

The activism of the cantadores and their admirers, associated in the coproduction of contexts in order to fulfill and perpetuate the repente, leads to an appropriation of the spaces in which they take the floor to mobilize consciences.

Mobility, Urbanization, and Expansion: Animation of a Territory

Without stopping, the cantadores go to meet new audiences that are also very mobile due to the characteristic emigration of the northeastern region. The climate of the semiarid Sertão, alternating a dry barren season and another favorable to crops, causes seasonal migrations. There are also exoduses due to exceptional droughts. There are other demographic, economic, and political reasons for this exodus: the Nordeste has the worst social indexes in the country, and a negative migration balance compared to all other regions. Groups of Nordestinos have participated in the exploitation of rubber trees in Amazonia, in the industrialization of the south of the country, and in the construction of its capitals, particularly in São Paulo, the largest city in South America. They followed the general patterns of urbanization, a major phenomenon of the last half century: in 1960, when the new futuristic capital Brasilia was built, for one Brazilian living in the city, four were living in the countryside. A few years ago the proportion was reversed. That led to a huge rural exodus in the Sertão, the birthplace of the tradition.

The cantadores always moved a lot, on foot or even horseback in the case of the oldest ones that I have interviewed, who were born during World War I. Nowadays, the mobility of the present ones is increasing due to sociodemographic transformations. By necessity, they have followed their public, and for the most part have become urbanized.

They have taken advantage of modern means of transport, communication, and information; they have conquered the new spaces of expression already mentioned; raised their educational level and their knowledge of the world; and have had to attend to more diversified requests from listeners confronting the reality of modernity. These new conditions of production have stimulated the cantadores, since the capacity to adapt is the source of an improviser's ability.

In a favorable context of rapid transformations, the poets say that they are experiencing a wonderful evolution, being more educated and organized than before.

Thousands of poets today live from their popular art within the confines of the vast space of Brazil, wherever they can magnify the Portuguese language in their poems, and wherever emigrants feel the need to organize a session in which the culture of the northeastern terroir will be sung.

The social dynamics shown by the poets have no equivalent in other genres of oral poetry in Brazil. According to my hypothesis, these genres do not give as much importance to improvisation, that is, its actors reiterate the forms instead of recreating them, and therefore do not encourage the necessary adaptations to the tradition.

In my research I became interested in other categories of popular poets. For example, by way of comparison, the coquistas testify that their number is declining, with less and less opportunities to sing. Yet they sing coco, a funnier genre than the repente, and one characterized by a fast tempo, catchy choruses, and entertaining street shows. In their duels, coquistas use a coarse language that is much appreciated and in the embolada, they exchange insults. But they improvise only partially, memorizing picaresque verses that skillfully adapt to the context and the reactions of the public. Often, they act as a way of animating open spaces (streets, squares, fairs), which are not conducive to commenting on serious topics. The listeners do not participate in the subject because coquistas do not take orders. They deliver little informative or critical content, unlike that heard in cantorias.

The two genres share some similarities: in both cases, it is poetry sung in a popular context. But they differ in terms of meaning. When there is more improvisation, a space can be created in which free and profound words can be exchanged. That is typical of the repentistas. I have shown that mobility of thinking is related to spatial mobility. It is also related to the desire for social transformation that a repentista possesses, wanting to be a spokesman for his Brazilian people.

In conclusion, the songs improvisers animate with represent a territory in which they divulge cultural elements typical of the region in which their art appeared. However, this territory in which today they are updating the tradition is limitless, due to the expansion of the repente by following the roads of northeastern emigration. Improvisers are mediators who promote adaptation strategies. They constantly produce and transform the elements of the culture of the people who listen to them.