Original: Teaching the Machine to Explain Itself using Domain Knowledge
Revised: Teaching the Machine to Explain Itself using Domain Knowledge

Original: Abstract
Revised: Abstract

Original: Machine Learning (ML) has been increasingly used to aid humans to make better and faster decisions.
Revised: Machine Learning (ML) has been increasingly used to aid humans to make better and faster decisions.

Original: However, non-technical humans-in-the-loop struggle to comprehend the rationale behind model predictions, hindering trust in algorithmic decision-making systems.
Revised: However, non-technical humans-in-the-loop struggle to comprehend the rationale behind model predictions, hindering trust in algorithmic decision-making systems.

Original: Considerable research work on AI explainability attempts to win back trust in AI systems by developing explanation methods but there is still no major breakthrough.
Revised: Considerable research work on AI explainability attempts to win back trust in AI systems by developing explanation methods but there is still no major breakthrough.

Original: At the same time, popular explanation methods ( e.g. ,
Revised: At the same time, popular explanation methods ( e.g. ,

Original: LIME, and SHAP) produce explanations that are very hard to understand for nondata scientist persona.
Revised: LIME, and SHAP) produce explanations that are very hard to understand for nondata scientist persona.

Original: To address this, we present JOEL, a neural network-based framework to jointly learn a decision-making task and associated explanations that convey domain knowledge.
Revised: To address this, we present JOEL, a neural network-based framework to jointly learn a decision-making task and associated explanations that convey domain knowledge.

Original: JOEL is tailored to human-in-the-loop domain experts that lack deep technical ML knowledge, providing high-level insights about the model’s predictions that very much resemble the experts’ own reasoning.
Revised: JOEL is tailored to human-in-the-loop domain experts that lack deep technical ML knowledge, providing high-level insights about the model’s predictions that very much resemble the experts’ own reasoning.

Original: Moreover, we collect the domain feedback from a pool of certiﬁed experts and use it to ameliorate the model (human teaching), hence promoting seamless and better suited explanations.
Revised: Moreover, we collect the domain feedback from a pool of certiﬁed experts and use it to ameliorate the model (human teaching), hence promoting seamless and better suited explanations.

Original: Lastly, we resort to semantic mappings between legacy expert systems and domain taxonomies to automatically annotate a bootstrap training set, overcoming the absence of concept-based human annotations.
Revised: Lastly, we resort to semantic mappings between legacy expert systems and domain taxonomies to automatically annotate a bootstrap training set, overcoming the absence of concept-based human annotations.

Original: We validate
Revised: We validate

Original: JOEL empirically on a real-world fraud detection dataset.
Revised: JOEL empirically on a real-world fraud detection dataset.

Original: We show that JOEL can generalize the explanations from the bootstrap dataset.
Revised: We show that JOEL can generalize the explanations from the bootstrap dataset.

Original: Furthermore, obtained results indicate that human teaching can further improve the explanations prediction quality by approximately 13 .
Revised: Furthermore, obtained results indicate that human teaching can further improve the explanations prediction quality by approximately 13 .

Original:  Introduction
Revised:  Introduction

Original: Notwithstanding the beneﬁts of Machine Learning (ML) commoditization in many areas several state-of-the-art ML methods convey recondite information about their predictions.
Revised: Notwithstanding the beneﬁts of Machine Learning (ML) commoditization in many areas, several state-of-the-art ML methods convey recondite information about their predictions.

Original: The inability to comprehend these outcomes hinders trust in these systems.
Revised: The inability to comprehend these outcomes hinders trust in these systems.

Original: Research on Explainable Artiﬁcial Intelligence (XAI) focuses on improving the interpretability (or transparency ) of these systems.
Revised: Research on Explainable Artiﬁcial Intelligence (XAI) focuses on improving the interpretability (or transparency ) of these systems.

Original: Whilst the XAI literature is booming with explainer methods [1, 2, 3, 4, 5], the large majority focus on the data scientist persona, creating low-level explanations that are too difﬁcult to grasp to other non-technical personas [6, 7, 8].
Revised: Whilst the XAI literature is booming with explainer methods [1, 2, 3, 4, 5], the large majority focus on the data scientist persona, creating low-level explanations that are too difﬁcult to grasp to other non-technical personas [6, 7, 8].

Original: This gap is witnessed in the recent impetus of feature-attribution-based explainers [1, 2, 3].
Revised: This gap is witnessed in the recent impetus of feature-attribution-based explainers [1, 2, 3].

Original: These build explanations as lists of input features and associated feature scores 1 .
Revised: These build explanations as lists of input features and associated feature scores 1 .

Original: However, these can be too speciﬁc and too technical.
Revised: However, these can be too speciﬁc and too technical.

Original: For this reason, explainer methods alike fail to address the information needs of domain experts , which are deeply involved in many human-AI cooperative systems [6, 8].
Revised: For this reason, explainer methods alike fail to address the information needs of domain experts , which are deeply involved in many human-AI cooperative systems [6, 8].

Original: Instead, it is necessary to create explanations capable of conveying domain knowledge.
Revised: Instead, it is necessary to create explanations capable of conveying domain knowledge.

Original: This should reﬂect the semantic concepts related to the task of the human-in-the-loop, encoding information about the domain experts underlying reasoning upon decision-making.
Revised: This should reﬂect the semantic concepts related to the task of the human-in-the-loop, encoding information about the domain experts underlying reasoning upon decision-making.

Original: Although recent lines of research have begun exploring “concept-based explanations” [7, 6, 8, 9] these do not apply to high stakes decision-making tasks.
Revised: Although recent lines of research have begun exploring “concept-based explanations” [7, 6, 8, 9] these do not apply to high stakes decision-making tasks.

Original: In this work, we aim to strengthen human-AI decision-making systems by: (1) proposing a novel
Revised: In this work, we aim to strengthen human-AI decision-making systems by: (1) proposing a novel

Original: XAI framework, JOEL, a self-explainable neural network that jointly learns semantic concepts and a decision associated with a predictive task [8, 10], and (2) exploiting the human-in-the-loop feedback about the decision task and explanations quality to continuously produce better suited explanations.
Revised: XAI framework, JOEL, a self-explainable neural network that jointly learns semantic concepts and a decision associated with a predictive task [8, 10], and (2) exploiting the human-in-the-loop feedback about the decision task and explanations quality to continuously produce better suited explanations.

Original: Figure 1 illustrates the proposed framework in a real world fraud detection setting.
Revised: Figure 1 illustrates the proposed framework in a real world fraud detection setting.

Original: The joint learning of the decision task and associated explanations leads to more robust and authenticexplanations.
Revised: The joint learning of the decision task and associated explanations leads to more robust and authenticexplanations.

Original: We encode the ML interpretability in an neural network (NN) model, hence allowing us to incorporate domain knowledge and to associate semantic concepts to the decision-making task [7, 6, 8, 9].
Revised: We encode the ML interpretability in an neural network (NN) model, hence allowing us to incorporate domain knowledge and to associate semantic concepts to the decision-making task [7, 6, 8, 9].

Original: These high-level concepts may encode external information that while not present in the data itself may be very useful.
Revised: These high-level concepts may encode external information that while not present in the data itself may be very useful.

Original: The human-AI proximity facilitates the creation of virtuous cycles where the model learns, through human (expert’s) feedback, how to improve its explanations in a way that beneﬁts the human at decision-making: the model learns the teacher ’s reasoning and with time it can foster faster and more effective decisions from the teacher .
Revised: The human-AI proximity facilitates the creation of virtuous cycles where the model learns, through human (expert’s) feedback, how to improve its explanations in a way that beneﬁts the human at decision-making: the model learns the teacher ’s reasoning and with time it can foster faster and more effective decisions from the teacher .

Original: Finally, we use a Distant Supervision approach to circumvent the low availability of human conceptlabeled datasets 3 when there are legacy expert systems in place ( e.g. , rule-based systems).
Revised: Finally, we use a Distant Supervision approach to circumvent the low availability of human conceptlabeled datasets 3 when there are legacy expert systems in place ( e.g. , rule-based systems).

Original: This approach automates the labeling process based on semantic mappings between the existing legacy system and domain knowledge taxonomies.
Revised: This approach automates the labeling process based on semantic mappings between the existing legacy system and domain knowledge taxonomies.

Original: The summary of our contributions are as follows:
Revised: The summary of our contributions are as follows:

Original: We present a novel self-explainable neural network framework to mutually learn a decision task and an explainability task, consisting of associated high-level domain concepts that serve as explanations to the human-in-the-loop (section 2.1); 2.
Revised: We present a novel self-explainable neural network framework to mutually learn a decision task and an explainability task, consisting of associated high-level domain concepts that serve as explanations to the human-in-the-loop (section 2.1); 2.

Original: We exploit the human-in-the-loop’s expertise to continuously tune the model on both tasks (section 2.2);
Revised: We exploit the human-in-the-loop’s expertise to continuously tune the model on both tasks (section 2.2);

Original: We propose and empirically validate the use of a semi-supervised approach for bootstrapping the proposed self-explainable neural network model when manually concept-based annotated datasets are unavailable (section 2.3).
Revised: We propose and empirically validate the use of a semi-supervised approach for bootstrapping the proposed self-explainable neural network model when manually concept-based annotated datasets are unavailable (section 2.3).

Original: We show on a real-world fraud dataset that JOEL is able to jointly learn the semantic concepts and the fraud decision task.
Revised: We show on a real-world fraud dataset that JOEL is able to jointly learn the semantic concepts and the fraud decision task.

Original: We observe an increase of the model’s explainability performance of approximately 13 .
Revised: We observe an increase of the model’s explainability performance of approximately 13 .

Original: 57% after the incorporation of expert’s feedback into the model (section 4).
Revised: 57% after the incorporation of expert’s feedback into the model (section 4).

Original:  Methodology
Revised:  Methodology

Original: In this section, we describe our framework for jointly learning a decision task and associated domain knowledge explanations.
Revised: In this section, we describe our framework for jointly learning a decision task and associated domain knowledge explanations.

Original: We assume the semantic concepts (used as explanations) will help the domain experts (end-users) reasoning throughout their decision-making process.
Revised: We assume the semantic concepts (used as explanations) will help the domain experts (end-users) reasoning throughout their decision-making process.

Original: Conversely, the framework beneﬁts from the domain experts’ feedback about which concepts justify their decisions.
Revised: Conversely, the framework beneﬁts from the domain experts’ feedback about which concepts justify their decisions.

Original: Hence, it can continuously improve both its predictive accuracy and explainability.
Revised: Hence, it can continuously improve both its predictive accuracy and explainability.

Original: We coin this framework Jointly learned cOncept-based ExpLanations (JOEL).
Revised: We coin this framework Jointly learned cOncept-based ExpLanations (JOEL).

Original: 2.1 Architecture
Revised: 2.1 Architecture

Original: We frame the problem of associating both semantic concepts and decision labels as one of ﬁnding an hypothesis (learner), h ∈ H , such that, for the same inputs, x ∈ X , h is able to simultaneously satisfy h : X → S and h : X → Y , where S is the set of semantic concepts and Y is the set of decisions (or classes) of the predictive task.
Revised: We frame the problem of associating both semantic concepts and decision labels as one of ﬁnding an hypothesis (learner), h ∈ H , such that, for the same inputs, x ∈ X , h is able to simultaneously satisfy h : X → S and h : X → Y , where S is the set of semantic concepts and Y is the set of decisions (or classes) of the predictive task.

Original: Conceptually, JOEL comprises three building blocks: (1) an NN, (2) a semantic layer, and (3) a decision layer.
Revised: Conceptually, JOEL comprises three building blocks: (1) an NN, (2) a semantic layer, and (3) a decision layer.

Original: NN’s versatility facilitates the arrangement of these components in multiple ways.
Revised: NN’s versatility facilitates the arrangement of these components in multiple ways.

Original: This work, however, delves into a hierarchical version (see ﬁgure 2): whose blocks are chained sequentially, i.e. , the outputs of the NN are fed as inputs to the semantic layer and its outputs are fed into the decision layer.
Revised: This work, however, delves into a hierarchical version (see ﬁgure 2): whose blocks are chained sequentially, i.e. , the outputs of the NN are fed as inputs to the semantic layer and its outputs are fed into the decision layer.

Original: JOEL outputs both decision predictions (last layer) and concept predictions.
Revised: JOEL outputs both decision predictions (last layer) and concept predictions.

Original: Vanilla NNs seek to model single-label tasks, such as fraud detection, or medical diagnosis.
Revised: Vanilla NNs seek to model single-label tasks, such as fraud detection, or medical diagnosis.

Original: JOEL’s hierarchical architecture can be seen as a multi-task materialization where the outputs of the ( L +1)
Revised: JOEL’s hierarchical architecture can be seen as a multi-task materialization where the outputs of the ( L +1)

Original: th layer are used both for a multi-label task (predict multiple semantic concepts) and a single-label task.
Revised: th layer are used both for a multi-label task (predict multiple semantic concepts) and a single-label task.

Original: By chaining the semantic and decision layers, we are able to encode external information about the domain which is not available in the feature data.
Revised: By chaining the semantic and decision layers, we are able to encode external information about the domain which is not available in the feature data.

Original: This is particularly meaningful when the taxonomy of semantic concepts is intimately related to the decision task ( e.g. , a fraud taxonomy of fraudulent patterns is deeply correlated with the fraud detection task).
Revised: This is particularly meaningful when the taxonomy of semantic concepts is intimately related to the decision task ( e.g. , a fraud taxonomy of fraudulent patterns is deeply correlated with the fraud detection task).

Original: Therefore, learning to accurately predict domain concepts is likely to also lead to better predictions and end-user decisions when it comes to the end-task.
Revised: Therefore, learning to accurately predict domain concepts is likely to also lead to better predictions and end-user decisions when it comes to the end-task.

Original: Similarly to other NN architectures, our method learns through backward propagation of errors ( backprop )[11] and some variant of the gradient descent method.
Revised: Similarly to other NN architectures, our method learns through backward propagation of errors ( backprop )[11] and some variant of the gradient descent method.

Original: Unlike other NNs models, the joint learning approach in JOEL’s method attempts to minimize both a decision loss , L D , and a semantic loss , L S .
Revised: Unlike other NNs models, the joint learning approach in JOEL’s method attempts to minimize both a decision loss , L D , and a semantic loss , L S .

Original: Mathematically, given model’s parameters, Θ =
Revised: Mathematically, given model’s parameters, Θ =

Original: [ θ 1 , θ 2 , ..., θ L , θ S , θ D ] , the outputs of the decision layer, δ D ( x, Θ) , and the outputs of semantic layer, δ S ( x, Θ) , the gradient with respect to the loss, L , at the semantic layer is given by equation 1.
Revised: [ θ 1 , θ 2 , ..., θ L , θ S , θ D ] , the outputs of the decision layer, δ D ( x, Θ) , and the outputs of semantic layer, δ S ( x, Θ) , the gradient with respect to the loss, L , at the semantic layer is given by equation 1.

Original: [Equation]
Revised: [Equation]

Original: where:
Revised: where:

Original: [Equation]
Revised: [Equation]

Original: The decision of which loss functions to use within each task depends on the nature of the task.
Revised: The decision of which loss functions to use within each task depends on the nature of the task.

Original: Since the semantic layer corresponds to a multi-labeling task, we use a sigmoid function for each individual entry of the output, before using it in the loss function.
Revised: Since the semantic layer corresponds to a multi-labeling task, we use a sigmoid function for each individual entry of the output, before using it in the loss function.

Original: To ﬁnd the mapping that simultaneously satisﬁes h : X → Y and h : X → S for a given input vector, x ∈ X , we mutually minimize the (categorical) cross-entropy for both predictive tasks.
Revised: To ﬁnd the mapping that simultaneously satisﬁes h : X → Y and h : X → S for a given input vector, x ∈ X , we mutually minimize the (categorical) cross-entropy for both predictive tasks.

Original: For an input vector, x ∈ X , a set of domain concepts, s ∈ S , and decision labels, y ∈ Y , we have that:
Revised: For an input vector, x ∈ X , a set of domain concepts, s ∈ S , and decision labels, y ∈ Y , we have that:

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: The real “intepretability” of concept-based explanations is debatable [8], mirroring the incertitude within the XAI ﬁeld.
Revised: The real “intepretability” of concept-based explanations is debatable [8], mirroring the incertitude within the XAI ﬁeld.

Original: From a fundamental standpoint, using semantic concepts to explain decisions does not necessarily signify the NN leverages their meaning in its predictive task.
Revised: From a fundamental standpoint, using semantic concepts to explain decisions does not necessarily signify the NN leverages their meaning in its predictive task.

Original: However, by design, we do expect the concepts to be intimately interrelated with the decision task.
Revised: However, by design, we do expect the concepts to be intimately interrelated with the decision task.

Original: In this view, JOEL’s explanations may actually bestow promising guidance to a skillful persona with no ML technical knowledge, thereby improving its efﬁciency and efﬁcacy.
Revised: In this view, JOEL’s explanations may actually bestow promising guidance to a skillful persona with no ML technical knowledge, thereby improving its efﬁciency and efﬁcacy.

Original: 2.2 Human Teaching
Revised: 2.2 Human Teaching

Original: Human teaching closes the human-AI feedback loop in algorithmic decision-making systems.
Revised: Human teaching closes the human-AI feedback loop in algorithmic decision-making systems.

Original: It is often the case that interactions within these systems are uni-directional: a human (typically, a domain expert) interacts with an ML system to make a decision.
Revised: It is often the case that interactions within these systems are uni-directional: a human (typically, a domain expert) interacts with an ML system to make a decision.

Original: However, while the ML system directly inﬂuences human decisions, the reverse is not veriﬁed, as most systems do not promptly adapt to human behavior.
Revised: However, while the ML system directly inﬂuences human decisions, the reverse is not veriﬁed, as most systems do not promptly adapt to human behavior.

Original: Instead, most systems are ofﬂine and it is only after a certain period of time that a new model is trained and adapted to the collected knowledge.
Revised: Instead, most systems are ofﬂine and it is only after a certain period of time that a new model is trained and adapted to the collected knowledge.

Original: JOEL tackles these limitations and tailors the explanations’ quality to be on par with the domain expert’s need.
Revised: JOEL tackles these limitations and tailors the explanations’ quality to be on par with the domain expert’s need.

Original: Since the explanations are meant to help the domain experts to better perform their task, these should also resemble the experts’ own reasoning.
Revised: Since the explanations are meant to help the domain experts to better perform their task, these should also resemble the experts’ own reasoning.

Original: As a result, the integration of expert feedback into the model’s learning process becomes vital to ensure that the explanations remain interpretable and that keep fostering more efﬁcient and effective decisions.
Revised: As a result, the integration of expert feedback into the model’s learning process becomes vital to ensure that the explanations remain interpretable and that keep fostering more efﬁcient and effective decisions.

Original: Figure 1 shows this mechanism in an illustrative example of a fraud detection task, where the main goal is to classify ﬁnancial transactions as being fraudulent or not.
Revised: Figure 1 shows this mechanism in an illustrative example of a fraud detection task, where the main goal is to classify ﬁnancial transactions as being fraudulent or not.

Original: After receiving a transaction, JOEL infers predictive scores for both the fraud decision and the fraud patterns (semantic concepts).
Revised: After receiving a transaction, JOEL infers predictive scores for both the fraud decision and the fraud patterns (semantic concepts).

Original: Using this information (explanation), a fraud analyst (domain expert) reviews the transaction and indicates which fraud patterns she based her decision.
Revised: Using this information (explanation), a fraud analyst (domain expert) reviews the transaction and indicates which fraud patterns she based her decision.

Original: Lastly, JOEL’s learnable parameters get updated based on the expert’s feedback.
Revised: Lastly, JOEL’s learnable parameters get updated based on the expert’s feedback.

Original: In many real-world settings, human expertise aims to disambiguate inputs for which the model is very uncertain.
Revised: In many real-world settings, human expertise aims to disambiguate inputs for which the model is very uncertain.

Original: In these cases, JOEL can exploit this short-term feedback to improve human-AI system performance.
Revised: In these cases, JOEL can exploit this short-term feedback to improve human-AI system performance.

Original: Our method’s hierarchical structure is likely to encode extra information based on this feedback.
Revised: Our method’s hierarchical structure is likely to encode extra information based on this feedback.

Original: Therefore, rapidly improving both its predictive accuracy and the explanations quality.
Revised: Therefore, rapidly improving both its predictive accuracy and the explanations quality.

Original: After bootstrapping JOEL using an initial concept-based annotated dataset, JOEL enters a human teaching stage , interacting with humans-in-the-loop: providing explanations and collecting human feedback about the generated explanations.
Revised: After bootstrapping JOEL using an initial concept-based annotated dataset, JOEL enters a human teaching stage , interacting with humans-in-the-loop: providing explanations and collecting human feedback about the generated explanations.

Original: Every b feedback instances, JOEL uses them to adjust its parameters through backprop .
Revised: Every b feedback instances, JOEL uses them to adjust its parameters through backprop .

Original: To prevent eventual model deterioration ( e.g. lower human aptitude for the task), we incorporate a quality control precondition for running this stage: to adhere to the pool of human teachers in the human-teaching stage, each human must meet a minimum accuracy-level (or other relevant qualitative measure).
Revised: To prevent eventual model deterioration ( e.g. lower human aptitude for the task), we incorporate a quality control precondition for running this stage: to adhere to the pool of human teachers in the human-teaching stage, each human must meet a minimum accuracy-level (or other relevant qualitative measure).

Original: 2.3 Distant Supervision
Revised: 2.3 Distant Supervision

Original: Training neural networks requires large amounts of data in order to attain reasonably good performance [12].
Revised: Training neural networks requires large amounts of data in order to attain reasonably good performance [12].

Original: Multi-label methods further exacerbate this data necessity.
Revised: Multi-label methods further exacerbate this data necessity.

Original: On top of that, difﬁculties associated with the collection and creation of concept-annotated datasets make NN explainers based on semantic concepts infeasible in many practical settings.
Revised: On top of that, difﬁculties associated with the collection and creation of concept-annotated datasets make NN explainers based on semantic concepts infeasible in many practical settings.

Original: JOEL demands large datasets to be able to generalize well.
Revised: JOEL demands large datasets to be able to generalize well.

Original: When there is no data available (or the data is insufﬁcient) and information based on legacy expert systems is available, we propose to bootstrap the model using a semi-supervised learning approach known as Distant Supervision [13, 14, 15].
Revised: When there is no data available (or the data is insufﬁcient) and information based on legacy expert systems is available, we propose to bootstrap the model using a semi-supervised learning approach known as Distant Supervision [13, 14, 15].

Original: Often times, these expert systems enclose semantic knowledge that can be associated with the concepts from the domain taxonomy.
Revised: Often times, these expert systems enclose semantic knowledge that can be associated with the concepts from the domain taxonomy.

Original: Therefore making it possible to automatically derive concepts for each datapoint without the need for human labels.
Revised: Therefore making it possible to automatically derive concepts for each datapoint without the need for human labels.

Original: While it still requires human effort to create the legacy systems-taxonomy mappings, the effort is negligible when compared with the effort of manually annotating a large dataset.
Revised: While it still requires human effort to create the legacy systems-taxonomy mappings, the effort is negligible when compared with the effort of manually annotating a large dataset.

Original: As an example consider the fraud prevention domain involving a rule legacy system.
Revised: As an example consider the fraud prevention domain involving a rule legacy system.

Original: Using the similarities between the domain knowledge conveyed in the rules, we create a rule-concept mapping to automatically associate rules to concepts in a fraud taxonomy.
Revised: Using the similarities between the domain knowledge conveyed in the rules, we create a rule-concept mapping to automatically associate rules to concepts in a fraud taxonomy.

Original: Table 1 shows examples of such mappings.
Revised: Table 1 shows examples of such mappings.

Original: After proper validation of the mapping by a fraud expert, we use it to automatically label payment transactions in bulk.
Revised: After proper validation of the mapping by a fraud expert, we use it to automatically label payment transactions in bulk.

Original: Speciﬁcally, consider a payment transaction X for which the legacy system triggered the two rules in the table.
Revised: Speciﬁcally, consider a payment transaction X for which the legacy system triggered the two rules in the table.

Original: The ﬁrst rule maps to the “Suspicious Items” and the second rule maps to “Suspicious Customer” and “Suspicious Payment”.
Revised: The ﬁrst rule maps to the “Suspicious Items” and the second rule maps to “Suspicious Customer” and “Suspicious Payment”.

Original: Thus, the Distant
Revised: Thus, the Distant

Original: Supervision technique automatically annotates transaction X with “Suspicious Items”, “Suspicious
Revised: Supervision technique automatically annotates transaction X with “Suspicious Items”, “Suspicious

Original: Customer”, and “Suspicious Payment”.
Revised: Customer”, and “Suspicious Payment”.

Original: [Table]
Revised: [Table]

Original: User tried n different cards last week
Revised: User tried n different cards last week

Original: [Table]
Revised: [Table]

Original: Although semi-supervised techniques improve scalability, one still requires a human-labeled dataset to empirically validate the results.
Revised: Although semi-supervised techniques improve scalability, one still requires a human-labeled dataset to empirically validate the results.

Original: As we will see in the ensuing section, we conduct an empirical validation of the proposed method using Distant Supervision to automatically label a dataset with 9 .million instances, followed by a validation in a smaller human-labeled dataset with 1561 transactions.
Revised: As we will see in the ensuing section, we conduct an empirical validation of the proposed method using Distant Supervision to automatically label a dataset with 9 .million instances, followed by a validation in a smaller human-labeled dataset with 1561 transactions.

Original:  Experimental Setup
Revised:  Experimental Setup

Original: We validate our explanation method empirically in a real Human-AI fraud prevention system.
Revised: We validate our explanation method empirically in a real Human-AI fraud prevention system.

Original: This system comprises a rule legacy system, a deployed ML model, and a pool of fraud analysts.
Revised: This system comprises a rule legacy system, a deployed ML model, and a pool of fraud analysts.

Original: In this context, when a payment transaction ﬁrst arrives, it undergoes a rule system, possibly triggering predeﬁned business rules ( e.g. , payment amount is anomalous or the IP of an online payment transaction is blacklisted).
Revised: In this context, when a payment transaction ﬁrst arrives, it undergoes a rule system, possibly triggering predeﬁned business rules ( e.g. , payment amount is anomalous or the IP of an online payment transaction is blacklisted).

Original: Subsequently, an ML model assigns the transaction a risk score.
Revised: Subsequently, an ML model assigns the transaction a risk score.

Original: The prevention system immediately issues a decision for the most extreme scores (accept low-risk and reject riskier ones).
Revised: The prevention system immediately issues a decision for the most extreme scores (accept low-risk and reject riskier ones).

Original: Accordingly, transactions whose score is within a business-deﬁned review band require human intervention.
Revised: Accordingly, transactions whose score is within a business-deﬁned review band require human intervention.

Original: In that case, fraud analysts inspect the transaction data, fraud risk, and the explanations, to decide whether to accept or reject the transaction.
Revised: In that case, fraud analysts inspect the transaction data, fraud risk, and the explanations, to decide whether to accept or reject the transaction.

Original: We evaluate JOEL in a two-step approach.
Revised: We evaluate JOEL in a two-step approach.

Original: We ﬁrst run a grid search over a “noisy dataset“ to determine the best architectural and learning decisions ( e.g. , number of hidden layers, number of neurons at each layer).
Revised: We ﬁrst run a grid search over a “noisy dataset“ to determine the best architectural and learning decisions ( e.g. , number of hidden layers, number of neurons at each layer).

Original: In total, we train 71 models.
Revised: In total, we train 71 models.

Original: Then, we ﬁx the decisions for the best model and we run another grid search over the “ground truth dataset“ (based on human labels) to determine the best conﬁguration possible for a human teaching setup and whether the models beneﬁt human teaching.
Revised: Then, we ﬁx the decisions for the best model and we run another grid search over the “ground truth dataset“ (based on human labels) to determine the best conﬁguration possible for a human teaching setup and whether the models beneﬁt human teaching.

Original: Finally, throughout these experiments, we examine the different trade-offs emerging from jointly learning two distinctive tasks: fraud detection (single-label task) and creation of explanations based on fraud patterns (multi-label task).
Revised: Finally, throughout these experiments, we examine the different trade-offs emerging from jointly learning two distinctive tasks: fraud detection (single-label task) and creation of explanations based on fraud patterns (multi-label task).

Original: We coin the ﬁrst stage Distant Supervision and the second
Revised: We coin the ﬁrst stage Distant Supervision and the second

Original: Human Teaching .
Revised: Human Teaching .

Original: Dataset : We use a privately held online retailer fraud detection dataset, comprising approximately 9 .
Revised: Dataset : We use a privately held online retailer fraud detection dataset, comprising approximately 9 .

Original: 3 millions of payment transactions of which only 2% represent fraudulent behaviors.
Revised: 3 millions of payment transactions of which only 2% represent fraudulent behaviors.

Original: For our experiment, we divide it into four sequential subsets: train, validation, test, and production.
Revised: For our experiment, we divide it into four sequential subsets: train, validation, test, and production.

Original: These represent 47% , 16% , 9% , 28% of the dataset and exhibit 2 .
Revised: These represent 47% , 16% , 9% , 28% of the dataset and exhibit 2 .

Original: 5% , 1 . 7% , 1 . 5% , 1 . 5% of fraud rate (prevalence), respectively.
Revised: 5% , 1 . 7% , 1 . 5% , 1 . 5% of fraud rate (prevalence), respectively.

Original: While training JOEL, we use the validation set to apply early stopping and set the outputs’ thresholds.
Revised: While training JOEL, we use the validation set to apply early stopping and set the outputs’ thresholds.

Original: Then, we perform model selection in the test set, after which only the best model is evaluated in a production set.
Revised: Then, we perform model selection in the test set, after which only the best model is evaluated in a production set.

Original: The production and test sets are apart by two months, thus mimicking practical settings where a large time gap exists between model selection and deployment.
Revised: The production and test sets are apart by two months, thus mimicking practical settings where a large time gap exists between model selection and deployment.

Original: Domain Knowledge taxonomy : With the help of skilful fraud analysts, we created a fraud taxonomy of suspicious patterns to guarantee that the provided explanations closely resemble their reasoning process throughout transactions reviewing.
Revised: Domain Knowledge taxonomy : With the help of skilful fraud analysts, we created a fraud taxonomy of suspicious patterns to guarantee that the provided explanations closely resemble their reasoning process throughout transactions reviewing.

Original: Rather than creating prosaic explanations, the taxonomy consists of keywords that correspond to certain ﬁnancial transaction patterns such as “malformed addresses” or “malformed name“.
Revised: Rather than creating prosaic explanations, the taxonomy consists of keywords that correspond to certain ﬁnancial transaction patterns such as “malformed addresses” or “malformed name“.

Original: Furthermore, the taxonomy is hierarchic, grouping patterns alike into more general concepts, for instance, by grouping the previous two patterns into the single broader concept “suspicious billing shipping”.
Revised: Furthermore, the taxonomy is hierarchic, grouping patterns alike into more general concepts, for instance, by grouping the previous two patterns into the single broader concept “suspicious billing shipping”.

Original: Semantically, these concepts refer to patterns involving speciﬁc information about the transaction and aim at pinpointing the most relevant information ( e.g. , evidence of wary behaviors) that the analysts look for when reviewing.
Revised: Semantically, these concepts refer to patterns involving speciﬁc information about the transaction and aim at pinpointing the most relevant information ( e.g. , evidence of wary behaviors) that the analysts look for when reviewing.

Original: As an example consider the concept “suspicious billing shipping” which aims to guide analysts’ attention to the information related with shipping or/and billing information and look for dubious aspects ( e.g. mismatch between addresses, and malformed address).
Revised: As an example consider the concept “suspicious billing shipping” which aims to guide analysts’ attention to the information related with shipping or/and billing information and look for dubious aspects ( e.g. mismatch between addresses, and malformed address).

Original: Distant Supervision Dataset (or machine-labeled dataset):
Revised: Distant Supervision Dataset (or machine-labeled dataset):

Original: In the absence of a human-labeled dataset, we use the Distant Supervision approach to automatically annotate the initial single-label dataset (exclusively annotated with fraud labels).
Revised: In the absence of a human-labeled dataset, we use the Distant Supervision approach to automatically annotate the initial single-label dataset (exclusively annotated with fraud labels).

Original: We extract the already existing information of a legacy rule system (that encodes high-level domain information) and map it into the fraudulent patterns in the taxonomy.
Revised: We extract the already existing information of a legacy rule system (that encodes high-level domain information) and map it into the fraudulent patterns in the taxonomy.

Original: We had domain specialists supervised this mappings, which totalsassociations.
Revised: We had domain specialists supervised this mappings, which totalsassociations.

Original: The result is a multi-label dataset: where each row is jointly associated with the fraud label (decision task) and fraud patterns (semantic concepts).
Revised: The result is a multi-label dataset: where each row is jointly associated with the fraud label (decision task) and fraud patterns (semantic concepts).

Original: Henceforth, given that these annotations are proxies of the true associated concepts, we dub them “noisy labels”.
Revised: Henceforth, given that these annotations are proxies of the true associated concepts, we dub them “noisy labels”.

Original: We observe a poor rule coverage of non-fraudulent patterns ( e.g. “Nothing Suspicious”, “Good Customer History”, “All
Revised: We observe a poor rule coverage of non-fraudulent patterns ( e.g. “Nothing Suspicious”, “Good Customer History”, “All

Original: Details Match”), which emphasizes the limitation of this approach on both the available rules.
Revised: Details Match”), which emphasizes the limitation of this approach on both the available rules.

Original: Human Labeled Dataset : A robust validation of our method should not depend exclusively on machine-labeled datasets (“noisy datasets”), for they are not guaranteed to reﬂect the real data.
Revised: Human Labeled Dataset : A robust validation of our method should not depend exclusively on machine-labeled datasets (“noisy datasets”), for they are not guaranteed to reﬂect the real data.

Original: Thus, we ran a human-labeling campaign involving three in-house fraud experts, where we mimic their original reviewing process.
Revised: Thus, we ran a human-labeling campaign involving three in-house fraud experts, where we mimic their original reviewing process.

Original: After each transaction review, the system prompts the analysts about the fraud patterns (or legitimate patterns) perceived during the transaction appraisal.
Revised: After each transaction review, the system prompts the analysts about the fraud patterns (or legitimate patterns) perceived during the transaction appraisal.

Original: Analysts select the concepts from the pool of concepts determined in the fraud taxonomy, and also the fraud decision.
Revised: Analysts select the concepts from the pool of concepts determined in the fraud taxonomy, and also the fraud decision.

Original: This human effort yielded 1561 manually labeled transactions.
Revised: This human effort yielded 1561 manually labeled transactions.

Original: Henceforth, we dub these labels as “ground truth” labels.
Revised: Henceforth, we dub these labels as “ground truth” labels.

Original: Evaluation Metrics : JOEL comprises two distinct tasks: a single-label task focused on discerning fraudulent from non-fraudulent transactions, and a multi-label task focused on producing a complete and informative explanation for the former.
Revised: Evaluation Metrics : JOEL comprises two distinct tasks: a single-label task focused on discerning fraudulent from non-fraudulent transactions, and a multi-label task focused on producing a complete and informative explanation for the former.

Original: Complying with the existing business constraints, which aim to reduce the probability of negatively affecting legitimate users, we set out to evaluate the ﬁrst task in terms of Recall at 3% False Positive Rate (FPR).
Revised: Complying with the existing business constraints, which aim to reduce the probability of negatively affecting legitimate users, we set out to evaluate the ﬁrst task in terms of Recall at 3% False Positive Rate (FPR).

Original: The evaluation results (after training and validation) require an aggregate measure of JOEL’s generalization at the multi-label task in holdout datasets.
Revised: The evaluation results (after training and validation) require an aggregate measure of JOEL’s generalization at the multi-label task in holdout datasets.

Original: We use the mean Area Under the Curve (AUC) over all predicted concepts because AUC gives a general idea about the overall performance for a single label.
Revised: We use the mean Area Under the Curve (AUC) over all predicted concepts because AUC gives a general idea about the overall performance for a single label.

Original: Baseline : We compare our hierarchical structure with the vanilla multi-label NN.
Revised: Baseline : We compare our hierarchical structure with the vanilla multi-label NN.

Original: In this baseline, instead of considering a hierarchical structure, we set both the decision and the semantic layer at the same level.
Revised: In this baseline, instead of considering a hierarchical structure, we set both the decision and the semantic layer at the same level.

Original: Hyperparameter Optimization best model criterion : From all the evaluated hyperparameter conﬁgurations (both for baseline and hierarchical structure), we select the one with highest performance at the fraud detection task.
Revised: Hyperparameter Optimization best model criterion : From all the evaluated hyperparameter conﬁgurations (both for baseline and hierarchical structure), we select the one with highest performance at the fraud detection task.

Original: This decision guarantees business constraints are met regardless of the explanations.
Revised: This decision guarantees business constraints are met regardless of the explanations.

Original: In case of ties, we discern the best model based on the performance at the second taskexplanations accuracy, measured on the Distant Supervision dataset.
Revised: In case of ties, we discern the best model based on the performance at the second taskexplanations accuracy, measured on the Distant Supervision dataset.

Original: Human Teaching : The second experiment uses the human-teaching stage, the human-labeled dataset, and the best architectural decisions as determined in the ﬁrst model selection stage.
Revised: Human Teaching : The second experiment uses the human-teaching stage, the human-labeled dataset, and the best architectural decisions as determined in the ﬁrst model selection stage.

Original: We carry out a small experiment to obtain proxy measures of the fraud analysts’ accuracy (prior to being shown explanations).
Revised: We carry out a small experiment to obtain proxy measures of the fraud analysts’ accuracy (prior to being shown explanations).

Original: All three analysts satisﬁed the minimum accuracy level of 0 .
Revised: All three analysts satisﬁed the minimum accuracy level of 0 .

Original: 5 set for this experiment (with 0 . 636 , 0 . 612 , and 0 .
Revised: 5 set for this experiment (with 0 . 636 , 0 . 612 , and 0 .

Original: 597 accuracy levels), and were, therefore, integrated in the teachers pool of the human-teaching stage.
Revised: 597 accuracy levels), and were, therefore, integrated in the teachers pool of the human-teaching stage.

Original: Afterwards, we split the human-labeled dataset into training, validation, and test sets.
Revised: Afterwards, we split the human-labeled dataset into training, validation, and test sets.

Original: The ﬁrst 800 transactions are used for training and the ensuing 200 for early stopping, threshold calibration, and model selection.
Revised: The ﬁrst 800 transactions are used for training and the ensuing 200 for early stopping, threshold calibration, and model selection.

Original: Then, we re-train the best performing model using both the training and validation sets.
Revised: Then, we re-train the best performing model using both the training and validation sets.

Original: We measure its generalization performance in an holdout test set composed of 561 transactions.
Revised: We measure its generalization performance in an holdout test set composed of 561 transactions.

Original:  Results and Discussion
Revised:  Results and Discussion

Original: Figure 3 shows the obtained trade-offs between decision task (fraud detection) and explainability task (predicting associated fraud concepts) for both JOEL and the baseline.
Revised: Figure 3 shows the obtained trade-offs between decision task (fraud detection) and explainability task (predicting associated fraud concepts) for both JOEL and the baseline.

Original: In total, we trained 71 distinct models for each architecture.
Revised: In total, we trained 71 distinct models for each architecture.

Original: In terms of the decision task, JOEL-derived architectures seem to exhibit a propensity for achieving high results in the predictive task, while also achieving competitive mean AUC values for the explainability task.
Revised: In terms of the decision task, JOEL-derived architectures seem to exhibit a propensity for achieving high results in the predictive task, while also achieving competitive mean AUC values for the explainability task.

Original: Conversely, although baseline models exhibit in some cases higher performance in the explainability task they fall short at the fraud prediction task.
Revised: Conversely, although baseline models exhibit in some cases higher performance in the explainability task they fall short at the fraud prediction task.

Original: In fact, only one baseline model ranks in the the top 15 best performing models on the decision task (ranks 12 th with 19 .
Revised: In fact, only one baseline model ranks in the the top 15 best performing models on the decision task (ranks 12 th with 19 .

Original: 95% of fraud Recall at 3% FPR).
Revised: 95% of fraud Recall at 3% FPR).

Original: Thus, given the business constraints, we select the best model in terms of fraud recall at 3% FPR, achieving 22 .
Revised: Thus, given the business constraints, we select the best model in terms of fraud recall at 3% FPR, achieving 22 .

Original: 81% Recall on the test set.
Revised: 81% Recall on the test set.

Original: The selected JOEL model has a total of 5 hidden layers (with 128 , 128 , 64 , 64 , and 32 neurons), a semantic layer (with 32 neurons), and a decision layer (with 14 neurons).
Revised: The selected JOEL model has a total of 5 hidden layers (with 128 , 128 , 64 , 64 , and 32 neurons), a semantic layer (with 32 neurons), and a decision layer (with 14 neurons).

Original: At each hidden layer, we use the ReLU activation function [16], followed by a dropout mechanism [17] (with rates of 40% , 40% , 30% , 30% , 20% ).
Revised: At each hidden layer, we use the ReLU activation function [16], followed by a dropout mechanism [17] (with rates of 40% , 40% , 30% , 30% , 20% ).

Original: We also apply a dropout rate of 20% to the semantic layer).
Revised: We also apply a dropout rate of 20% to the semantic layer).

Original: Finally, we use the out-of-the box Adam optimizer [18] and set the learning rate to 0 . 0001 .
Revised: Finally, we use the out-of-the box Adam optimizer [18] and set the learning rate to 0 . 0001 .

Original: Distant Supervision results: We use Distant Supervision to bootstrap our models with “noisy labels”.
Revised: Distant Supervision results: We use Distant Supervision to bootstrap our models with “noisy labels”.

Original: As a result, the quality of the model’s predictions is directly correlated with the rule-concepts mapping: if a concept does not have a matching rule, then the model will suffer from the cold-start problem 4 .
Revised: As a result, the quality of the model’s predictions is directly correlated with the rule-concepts mapping: if a concept does not have a matching rule, then the model will suffer from the cold-start problem 4 .

Original: Even if there exist rules that map to each concept, if they exhibit small prevalence in the dataset, then it is likely that the model struggles to learn them.
Revised: Even if there exist rules that map to each concept, if they exhibit small prevalence in the dataset, then it is likely that the model struggles to learn them.

Original: This concepts’ misrepresentation reﬂected on the obtained results.
Revised: This concepts’ misrepresentation reﬂected on the obtained results.

Original: Table 2 shows the results of the best JOEL model (with 0 .
Revised: Table 2 shows the results of the best JOEL model (with 0 .

Original: 8446 mean
Revised: 8446 mean

Original: AUC) on the hold-out Distant Supervision production dataset discriminated by semantic concept.
Revised: AUC) on the hold-out Distant Supervision production dataset discriminated by semantic concept.

Original: Although the model attains AUC’s above 0 .
Revised: Although the model attains AUC’s above 0 .

Original: 80 for most concepts, for two of them the performance is worse than 0 .
Revised: 80 for most concepts, for two of them the performance is worse than 0 .

Original: We hypothesize this to be related with the concept’s misrepresentation.
Revised: We hypothesize this to be related with the concept’s misrepresentation.

Original: In particular, we emphasize that the rule system is tailored for anomalous behavior detection, with only a small fraction of the rules mapping to legitimate behaviors.
Revised: In particular, we emphasize that the rule system is tailored for anomalous behavior detection, with only a small fraction of the rules mapping to legitimate behaviors.

Original: Therefore, with the exception of the “Suspicious
Revised: Therefore, with the exception of the “Suspicious

Original: Delivery” concept
Revised: Delivery” concept

Original: , the model exhibits fairly high AUC values for fraud concepts.
Revised: , the model exhibits fairly high AUC values for fraud concepts.

Original: Since only 5 rules map to “Suspicious Delivery”, all of which underrepresented, the model is not able to generalize it.
Revised: Since only 5 rules map to “Suspicious Delivery”, all of which underrepresented, the model is not able to generalize it.

Original: [Table]
Revised: [Table]

Original: Human teaching results :
Revised: Human teaching results :

Original: In the second stage of our experiment, we start with the evaluation of the best model in the previous stage on a human labeled test set, containing 561 instances.
Revised: In the second stage of our experiment, we start with the evaluation of the best model in the previous stage on a human labeled test set, containing 561 instances.

Original: This evaluation comprises no hyperparameter or structural tweaks.
Revised: This evaluation comprises no hyperparameter or structural tweaks.

Original: We then compare this approach with one where the model tweaks its hyperparameters based on the human teachers’ feedback.
Revised: We then compare this approach with one where the model tweaks its hyperparameters based on the human teachers’ feedback.

Original: For this teaching stage, we increase the model’s learning rate from 0 .
Revised: For this teaching stage, we increase the model’s learning rate from 0 .

Original: 0001 to 0 .
Revised: 0001 to 0 .

Original: 05 , hence causing the model’s weights to be updated more aggressively.
Revised: 05 , hence causing the model’s weights to be updated more aggressively.

Original: Table 3) shows the obtained AUC results for the former untweaked model (“no human teaching”) and also for the model after the human teaching stage (“human teaching”).
Revised: Table 3) shows the obtained AUC results for the former untweaked model (“no human teaching”) and also for the model after the human teaching stage (“human teaching”).

Original: We observe a small improvement of approximately 0 .
Revised: We observe a small improvement of approximately 0 .

Original: 04 for the “Suspicious Delivery” and “All details match” concepts, for which the Distant Supervision approach had an initially worse performance.
Revised: 04 for the “Suspicious Delivery” and “All details match” concepts, for which the Distant Supervision approach had an initially worse performance.

Original: Interestingly, it is also possible to observe a detriment in the predictive performance of the concept “High speed ordering”, “Suspicious Customer”, “Nothing Suspicious”, and “Good customer history”.
Revised: Interestingly, it is also possible to observe a detriment in the predictive performance of the concept “High speed ordering”, “Suspicious Customer”, “Nothing Suspicious”, and “Good customer history”.

Original: One possible cause of this result may be incongruent mental representations between human experts.
Revised: One possible cause of this result may be incongruent mental representations between human experts.

Original: A possible way to mitigate it, would be to implement an agreement-based ﬁltering technique that would be responsible for ﬁltering out the human feedback based on the agreement of multiple teachers.
Revised: A possible way to mitigate it, would be to implement an agreement-based ﬁltering technique that would be responsible for ﬁltering out the human feedback based on the agreement of multiple teachers.

Original: [Table]
Revised: [Table]

Original: Overall, we can observe that (1) the pre-trained model with Distant Supervision approach is able to generalize the explanations, and (2) the human feedback enhances, on average, the model’s explanation quality, improving the predictive performance of concepts it initially struggled to predict.
Revised: Overall, we can observe that (1) the pre-trained model with Distant Supervision approach is able to generalize the explanations, and (2) the human feedback enhances, on average, the model’s explanation quality, improving the predictive performance of concepts it initially struggled to predict.

Original:  Related Work
Revised:  Related Work

Original: The use of human-friendly semantic concepts is becoming more popular, speciﬁcally, in the computer vision domain.
Revised: The use of human-friendly semantic concepts is becoming more popular, speciﬁcally, in the computer vision domain.

Original: For instance, Kim et al.
Revised: For instance, Kim et al.

Original: [7] proposed Testing with Concept Activation Vectors (TCAV), a concept-based explainer that weighs the importance of a user-deﬁned concept to a classiﬁcation result ( e.g. , how sensitive the prediction of “bird” is to the presence of concepts like “feathers” or “beak”).
Revised: [7] proposed Testing with Concept Activation Vectors (TCAV), a concept-based explainer that weighs the importance of a user-deﬁned concept to a classiﬁcation result ( e.g. , how sensitive the prediction of “bird” is to the presence of concepts like “feathers” or “beak”).

Original: Recently, this method has also been applied to the natural language domain, for example, to measure how neural network activation units react to different human values-laden terms such as “respective”, “abusive”, “straight” among others [19].
Revised: Recently, this method has also been applied to the natural language domain, for example, to measure how neural network activation units react to different human values-laden terms such as “respective”, “abusive”, “straight” among others [19].

Original: Also in the computer vision domain, Ghorbani et al.
Revised: Also in the computer vision domain, Ghorbani et al.

Original: [6] propose Automated Concept-Based Explanation (ACE), that extracts concepts directly from the input data without any external source.
Revised: [6] propose Automated Concept-Based Explanation (ACE), that extracts concepts directly from the input data without any external source.

Original: It ﬁrst obtains different embeddings for different image segments and then applies a clustering algorithm in the embeddings space to identify the concepts.
Revised: It ﬁrst obtains different embeddings for different image segments and then applies a clustering algorithm in the embeddings space to identify the concepts.

Original: In another perspective, Melis and Jaakkola [8] include the interpretability straight into the ML model architecture (dubbed self-explainer concept-based model).
Revised: In another perspective, Melis and Jaakkola [8] include the interpretability straight into the ML model architecture (dubbed self-explainer concept-based model).

Original: They use autoencoders to generate the basis concepts and their relevance scores from low-level input features, which then serve as input to the predictive task.
Revised: They use autoencoders to generate the basis concepts and their relevance scores from low-level input features, which then serve as input to the predictive task.

Original: In another vein, DoctorXAI [9] leverages domain knowledge to produce more comprehensive explanations in medical diagnosis tasks.
Revised: In another vein, DoctorXAI [9] leverages domain knowledge to produce more comprehensive explanations in medical diagnosis tasks.

Original: This domain information is used to guide the explanation generation process of a low-level explainer, LIME [1].
Revised: This domain information is used to guide the explanation generation process of a low-level explainer, LIME [1].

Original: All in all, most concept-based methods in the literature are speciﬁc to computer vision.
Revised: All in all, most concept-based methods in the literature are speciﬁc to computer vision.

Original: Indeed, there is currently no research work in what it comes to high-level concept-based explanations for decision-making tasks.
Revised: Indeed, there is currently no research work in what it comes to high-level concept-based explanations for decision-making tasks.

Original: While DoctorXAI [9] uses concepts to create explanations for a decisionmaking setting, the explanation itself does not convey any domain knowledge about the decision but rather about the feature space.
Revised: While DoctorXAI [9] uses concepts to create explanations for a decisionmaking setting, the explanation itself does not convey any domain knowledge about the decision but rather about the feature space.

Original: Hence, it fails to address information needs of non-data scientist humans-in-the-loop.
Revised: Hence, it fails to address information needs of non-data scientist humans-in-the-loop.

Original: A crucial aspect in interpretable concept learning concerns the data quality, since many of the previous methods demand for largely annotated datasets.
Revised: A crucial aspect in interpretable concept learning concerns the data quality, since many of the previous methods demand for largely annotated datasets.

Original: Unfortunately, obtaining enough human annotations can be difﬁcult in practice due to the time and efforts required.
Revised: Unfortunately, obtaining enough human annotations can be difﬁcult in practice due to the time and efforts required.

Original: In this regard, semi-supervised learning approaches constitute a promising bypass to the label shortage [13, 14, 20, 21, 22].
Revised: In this regard, semi-supervised learning approaches constitute a promising bypass to the label shortage [13, 14, 20, 21, 22].

Original: Most of these approaches foster the programmatic labeling of datasets.
Revised: Most of these approaches foster the programmatic labeling of datasets.

Original: One such example is the Distant supervision technique [13, 14], which leverages existing knowledge bases ( e.g. , rule systems) and, through the heuristically creation of semantic mappings between the knowledge bases and the concepts, facilitates the automatic (weak) annotation of the dataset.
Revised: One such example is the Distant supervision technique [13, 14], which leverages existing knowledge bases ( e.g. , rule systems) and, through the heuristically creation of semantic mappings between the knowledge bases and the concepts, facilitates the automatic (weak) annotation of the dataset.

Original: Another emerging technique in the natural language domain is the joint use of written explanations and labeling functions to train a generative labelling model [21, 22].
Revised: Another emerging technique in the natural language domain is the joint use of written explanations and labeling functions to train a generative labelling model [21, 22].

Original: In this context, a small subset is annotated with natural language explanations and decision labels.
Revised: In this context, a small subset is annotated with natural language explanations and decision labels.

Original: These, along with the instances, are then used to hot-start and parametrize the labeling functions that will be responsible for generating the labels.
Revised: These, along with the instances, are then used to hot-start and parametrize the labeling functions that will be responsible for generating the labels.

Original: In our experiments, we use the distant supervision approach to exploit the already available vestiges of a legacy rule-based system in the data.
Revised: In our experiments, we use the distant supervision approach to exploit the already available vestiges of a legacy rule-based system in the data.

Original: We leave the exploration of other approaches for future work.
Revised: We leave the exploration of other approaches for future work.

Original:  Conclusion and Future Work
Revised:  Conclusion and Future Work

Original: In this work, we propose JOEL, an NN-based framework for jointly learning a decision task and associated domain knowledge explanations.
Revised: In this work, we propose JOEL, an NN-based framework for jointly learning a decision task and associated domain knowledge explanations.

Original: Simultaneously, our framework exploits the decisionmakers feedback regarding not only their decision but also the concepts that justify their decision.
Revised: Simultaneously, our framework exploits the decisionmakers feedback regarding not only their decision but also the concepts that justify their decision.

Original: Initial experiments show that using human teaching can lead to signiﬁcant gains in predictive accuracy and explainability.
Revised: Initial experiments show that using human teaching can lead to signiﬁcant gains in predictive accuracy and explainability.

Original: Moreover, we show that JOEL is able to overcome the cold-start problem and generalize from a machine-labeled dataset when using a Distant Supervision technique.
Revised: Moreover, we show that JOEL is able to overcome the cold-start problem and generalize from a machine-labeled dataset when using a Distant Supervision technique.

Original: Future directions include validating JOEL in other settings ( e.g. , in the healthcare domain), robustly assessing the framework’s true impact and utility for the users, exploring distinct NN variants ( e.g. , architectures, loss functions), and exploring incremental learning to continuously adapt to new knowledge.
Revised: Future directions include validating JOEL in other settings ( e.g. , in the healthcare domain), robustly assessing the framework’s true impact and utility for the users, exploring distinct NN variants ( e.g. , architectures, loss functions), and exploring incremental learning to continuously adapt to new knowledge.

Original:  Acknowledgements
Revised:  Acknowledgements

Original: The project CAMELOT (reference POCI-01-0247-FEDER-045915) leading to this work is coﬁnanced by the ERDF - European Regional Development Fund through the Operational Program for
Revised: The project CAMELOT (reference POCI-01-0247-FEDER-045915) leading to this work is coﬁnanced by the ERDF - European Regional Development Fund through the Operational Program for

Original: Competitiveness and Internationalisation - COMPETE 2020, the North Portugal Regional Operational
Revised: Competitiveness and Internationalisation - COMPETE 2020, the North Portugal Regional Operational

Original: Program - NORTE 2020 and by the Portuguese Foundation for Science and Technology - FCT under the CMU Portugal international partnership
Revised: Program - NORTE 2020 and by the Portuguese Foundation for Science and Technology - FCT under the CMU Portugal international partnership