Notwithstanding the benefits of Machine Learning (ML) commoditization in many areas, several state-of-the-art ML methods convey recondite information about their predictions. The inability to comprehend these outcomes hinders trust in these systems.
Research on Explainable Artificial Intelligence
Explainable Artificial Intelligence(XAI) focuses on improving the interpretability (or transparency) of these systems. Whilst the XAI literature is booming with explainer methods Ribeiro et al. (2016); Lundberg and Lee (2017); Plumb (2018); Ribeiro et al. (2018); Zafar (2019), the large majority focus on the data scientist persona, creating low-level explanations that are too difficult to grasp to other non-technical personas Ghorbani et al. (2019); Kim et al. (2018); Melis and Jaakkola (2018). This gap is witnessed in the recent impetus of feature-attribution-based explainers Ribeiro et al. (2016); Lundberg and Lee (2017); Plumb (2018). These build explanations as lists of input features and associated feature scores111The feature’s contribution to a given ML prediction. However, these can be too specific and too technical. 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 Ghorbani et al. (2019); Melis and Jaakkola (2018).
Instead, it is necessary to create explanations capable of conveying domain knowledge. 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. Although recent lines of research have begun exploring “concept-based explanations” Kim et al. (2018); Ghorbani et al. (2019); Melis and Jaakkola (2018); Panigutti et al. (2020) these do not apply to high stakes decision-making tasks.
In this work, we aim to strengthen human-AI decision-making systems by: (1) proposing a novel XAI framework, JOEL, a self-explainable neural network that jointly learns semantic concepts and a decision associated with a predictive task Melis and Jaakkola (2018); Elton (2020), and (2) exploiting the human-in-the-loop feedback about the decision task and explanations quality to continuously produce better suited explanations. Figure 1 illustrates the proposed framework in a real world fraud detection setting.
The joint learning of the decision task and associated explanations leads to more robust and authentic222In this context, we refer to authenticity as being a true reason for a given ML outcome. explanations. 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 Kim et al. (2018); Ghorbani et al. (2019); Melis and Jaakkola (2018); Panigutti et al. (2020). These high-level concepts may encode external information that while not present in the data itself may be very useful.
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.
Finally, we use a Distant Supervision approach to circumvent the low availability of human concept-labeled datasets333Manually creating these datasets carries abnormal costs and time efforts.Melis and Jaakkola (2018) when there are legacy expert systems in place (e.g.
, rule-based systems). This approach automates the labeling process based on semantic mappings between the existing legacy system and domain knowledge taxonomies.
The summary of our contributions are as follows:
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);
We exploit the human-in-the-loop’s expertise to continuously tune the model on both tasks (section 2.2);
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).
We show on a real-world fraud dataset that JOEL is able to jointly learn the semantic concepts and the fraud decision task. We observe an increase of the model’s explainability performance of approximately after the incorporation of expert’s feedback into the model (section 4).
In this section, we describe our framework for jointly learning a decision task and associated domain knowledge explanations. We assume the semantic concepts (used as explanations) will help the domain experts (end-users) reasoning throughout their decision-making process. Conversely, the framework benefits from the domain experts’ feedback about which concepts justify their decisions. Hence, it can continuously improve both its predictive accuracy and explainability. We coin this framework Jointly learned cOncept-based ExpLanations (JOEL).
We frame the problem of associating both semantic concepts and decision labels as one of finding an hypothesis (learner), , such that, for the same inputs, , is able to simultaneously satisfy and , where is the set of semantic concepts and is the set of decisions (or classes) of the predictive task.
Conceptually, JOEL comprises three building blocks: (1) an NN, (2) a semantic layer, and (3) a decision layer. NN’s versatility facilitates the arrangement of these components in multiple ways. 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. JOEL outputs both decision predictions (last layer) and concept predictions.
Vanilla NNs seek to model single-label tasks, such as fraud detection, or medical diagnosis. JOEL’s hierarchical architecture can be seen as a multi-task materialization where the outputs of the layer are used both for a multi-label task (predict multiple semantic concepts) and a single-label task. 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. 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). 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.
Similarly to other NN architectures, our method learns through backward propagation of errors (backprop)Rumelhart et al. (1986) and some variant of the gradient descent method. Unlike other NNs models, the joint learning approach in JOEL’s method attempts to minimize both a decision loss, , and a semantic loss, . Mathematically, given model’s parameters, , the outputs of the decision layer, , and the outputs of semantic layer, , the gradient with respect to the loss, , at the semantic layer is given by equation 1.
The decision of which loss functions to use within each task depends on the nature of the task. Since the semantic layer corresponds to a multi-labeling task, we use asigmoid function for each individual entry of the output, before using it in the loss function. To find the mapping that simultaneously satisfies and for a given input vector, , we mutually minimize the (categorical) cross-entropy for both predictive tasks. For an input vector, , a set of domain concepts, , and decision labels, , we have that:
The real “intepretability” of concept-based explanations is debatable Melis and Jaakkola (2018), mirroring the incertitude within the XAI field. From a fundamental standpoint, using semantic concepts to explain decisions does not necessarily signify the NN leverages their meaning in its predictive task. However, by design, we do expect the concepts to be intimately interrelated with the decision task. 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.
2.2 Human Teaching
Human teaching closes the human-AI feedback loop in algorithmic decision-making systems. 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. However, while the ML system directly influences human decisions, the reverse is not verified, as most systems do not promptly adapt to human behavior. 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.
JOEL tackles these limitations and tailors the explanations’ quality to be on par with the domain expert’s need. Since the explanations are meant to help the domain experts to better perform their task, these should also resemble the experts’ own reasoning. 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.
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. After receiving a transaction,JOEL infers predictive scores for both the fraud decision and the fraud patterns (semantic concepts). Using this information (explanation), a fraud analyst (domain expert) reviews the transaction and indicates which fraud patterns she based her decision. Lastly, JOEL’s learnable parameters get updated based on the expert’s feedback.
In many real-world settings, human expertise aims to disambiguate inputs for which the model is very uncertain. In these cases, JOEL can exploit this short-term feedback to improve human-AI system performance. Our method’s hierarchical structure is likely to encode extra information based on this feedback. Therefore, rapidly improving both its predictive accuracy and the explanations quality.
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. Every feedback instances, JOEL uses them to adjust its parameters through backprop. 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).
2.3 Distant Supervision
Training neural networks requires large amounts of data in order to attain reasonably good performance Roh et al. (2019). Multi-label methods further exacerbate this data necessity. 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.
JOEL demands large datasets to be able to generalize well. 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 asDistant Supervision Mintz et al. (2009); Go et al. (2009); Zeng et al. (2015). Often times, these expert systems enclose semantic knowledge that can be associated with the concepts from the domain taxonomy. Therefore making it possible to automatically derive concepts for each datapoint without the need for human labels. 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.
As an example consider the fraud prevention domain involving a rule legacy system. 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. Table 1 shows examples of such mappings. After proper validation of the mapping by a fraud expert, we use it to automatically label payment transactions in bulk. Specifically, consider a payment transaction for which the legacy system triggered the two rules in the table. The first rule maps to the “Suspicious Items” and the second rule maps to “Suspicious Customer” and “Suspicious Payment”. Thus, the Distant Supervision technique automatically annotates transaction with “Suspicious Items”, “Suspicious Customer”, and “Suspicious Payment”.
|Rule Description||Mapped Concepts|
|Order contains risky product styles.||Suspicious Items|
|User tried n different cards last week.||Suspicious Customer, Suspicious Payment|
Although semi-supervised techniques improve scalability, one still requires a human-labeled dataset to empirically validate the results. 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 million instances, followed by a validation in a smaller human-labeled dataset with transactions.
3 Experimental Setup
We validate our explanation method empirically in a real Human-AI fraud prevention system. This system comprises a rule legacy system, a deployed ML model, and a pool of fraud analysts. In this context, when a payment transaction first arrives, it undergoes a rule system, possibly triggering pre-defined business rules (e.g., payment amount is anomalous or the IP of an online payment transaction is blacklisted). Subsequently, an ML model assigns the transaction a risk score. The prevention system immediately issues a decision for the most extreme scores (accept low-risk and reject riskier ones). Accordingly, transactions whose score is within a business-defined review band require human intervention. In that case, fraud analysts inspect the transaction data, fraud risk, and the explanations, to decide whether to accept or reject the transaction.
We evaluate JOEL in a two-step approach. 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). In total, we trainmodels. 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. 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). We coin the first stage Distant Supervision and the second Human Teaching.
Dataset: We use a privately held online retailer fraud detection dataset, comprising approximately millions of payment transactions of which only represent fraudulent behaviors. For our experiment, we divide it into four sequential subsets: train, validation, test, and production. These represent , , , of the dataset and exhibit , , , of fraud rate (prevalence), respectively. While training JOEL, we use the validation set to apply early stopping and set the outputs’ thresholds. Then, we perform model selection in the test set, after which only the best model is evaluated in a production set. 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.
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. Rather than creating prosaic explanations, the taxonomy consists of keywords that correspond to certain financial transaction patterns such as “malformed addresses” or “malformed name“. 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”. 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. 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).
Distant Supervision Dataset (or machine-labeled dataset): 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). 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. We had domain specialists supervised this mappings, which totals associations.The result is a multi-label dataset: where each row is jointly associated with the fraud label (decision task) and fraud patterns (semantic concepts). Henceforth, given that these annotations are proxies of the true associated concepts, we dub them “noisy labels”. We observe a poor rule coverage of non-fraudulent patterns (e.g.“Nothing Suspicious”, “Good Customer History”, “All Details Match”), which emphasizes the limitation of this approach on both the available rules.
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. Thus, we ran a human-labeling campaign involving three in-house fraud experts, where we mimic their original reviewing process. After each transaction review, the system prompts the analysts about the fraud patterns (or legitimate patterns) perceived during the transaction appraisal. Analysts select the concepts from the pool of concepts determined in the fraud taxonomy, and also the fraud decision. This human effort yielded manually labeled transactions. Henceforth, we dub these labels as “ground truth” labels.
: 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. 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 atFalse Positive Rate (FPR). The evaluation results (after training and validation) require an aggregate measure of JOEL’s generalization at the multi-label task in holdout datasets. 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.
Baseline: We compare our hierarchical structure with the vanilla multi-label NN. In this baseline, instead of considering a hierarchical structure, we set both the decision and the semantic layer at the same level.
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. This decision guarantees business constraints are met regardless of the explanations. In case of ties, we discern the best model based on the performance at the second task - explanations accuracy, measured on the Distant Supervision dataset.
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. We carry out a small experiment to obtain proxy measures of the fraud analysts’ accuracy (prior to being shown explanations). All three analysts satisfied the minimum accuracy level of set for this experiment (with , , and accuracy levels), and were, therefore, integrated in the teachers pool of the human-teaching stage. Afterwards, we split the human-labeled dataset into training, validation, and test sets. The first transactions are used for training and the ensuing for early stopping, threshold calibration, and model selection. Then, we re-train the best performing model using both the training and validation sets. We measure its generalization performance in an holdout test set composed of transactions.
4 Results and Discussion
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. In total, we trained distinct models for each architecture. 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. Conversely, although baseline models exhibit in some cases higher performance in the explainability task they fall short at the fraud prediction task. In fact, only one baseline model ranks in the the top best performing models on the decision task (ranks with of fraud Recall at FPR).
Thus, given the business constraints, we select the best model in terms of fraud recall at FPR, achieving Recall on the test set. The selected JOEL model has a total of hidden layers (with , , , , and neurons), a semantic layer (with neurons), and a decision layer (with neurons). At each hidden layer, we use the ReLUactivation function Zhang et al. (2018), followed by a dropout mechanism Srivastava et al. (2014) (with rates of , , , , ). We also apply a dropout rate of to the semantic layer). Finally, we use the out-of-the box Adam optimizer Kingma and Ba (2014) and set the learning rate to .
Distant Supervision results: We use Distant Supervision to bootstrap our models with “noisy labels”. 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 problem444Other semi-supervised techniques discussed in section 5 can be used to overcome the cold-start problem.. 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. This concepts’ misrepresentation reflected on the obtained results. Table 2 shows the results of the best JOEL model (with mean AUC) on the hold-out Distant Supervision production dataset discriminated by semantic concept. Although the model attains AUC’s above for most concepts, for two of them the performance is worse than . We hypothesize this to be related with the concept’s misrepresentation. 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. Therefore, with the exception of the “Suspicious Delivery” concept, the model exhibits fairly high AUC values for fraud concepts. Since only rules map to “Suspicious Delivery”, all of which underrepresented, the model is not able to generalize it.
|Suspicious billing shipping||0.8921|
|High speed ordering||0.9468|
|All details match||0.6470|
|Good customer history||0.8370|
Human teaching results: 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 instances. This evaluation comprises no hyperparameter or structural tweaks. We then compare this approach with one where the model tweaks its hyperparameters based on the human teachers’ feedback. For this teaching stage, we increase the model’s learning rate from to , hence causing the model’s weights to be updated more aggressively. 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”). We observe a small improvement of approximately for the “Suspicious Delivery” and “All details match” concepts, for which the Distant Supervision approach had an initially worse performance. 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”. One possible cause of this result may be incongruent mental representations between human experts. 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.
|Concept||No Human Teaching (AUC)||Human Teaching (AUC)|
|Suspicious billing shipping||0.5943||0.7531|
|High speed ordering||0.7365||0.6874|
|All details match||0.3496||0.3976|
|Good customer history||0.4250||0.3266|
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.
5 Related Work
The use of human-friendly semantic concepts is becoming more popular, specifically, in the computer vision domain. For instance, Kimet al.Kim et al. (2018) 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”). 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 Hutchinson et al. (2019). Also in the computer vision domain, Ghorbani et al.Ghorbani et al. (2019) propose Automated Concept-Based Explanation (ACE), that extracts concepts directly from the input data without any external source. It first obtains different embeddings for different image segments and then applies a clustering algorithm in the embeddings space to identify the concepts.
In another perspective, Melis and Jaakkola Melis and Jaakkola (2018) include the interpretability straight into the ML
model architecture (dubbed self-explainer concept-based model). 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. In another vein,DoctorXAI Panigutti et al. (2020) leverages domain knowledge to produce more comprehensive explanations in medical diagnosis tasks. This domain information is used to guide the explanation generation process of a low-level explainer, LIME Ribeiro et al. (2016).
All in all, most concept-based methods in the literature are specific to computer vision. Indeed, there is currently no research work in what it comes to high-level concept-based explanations for decision-making tasks. While DoctorXAI Panigutti et al. (2020) uses concepts to create explanations for a decision-making setting, the explanation itself does not convey any domain knowledge about the decision but rather about the feature space. Hence, it fails to address information needs of non-data scientist humans-in-the-loop.
A crucial aspect in interpretable concept learning concerns the data quality, since many of the previous methods demand for largely annotated datasets. Unfortunately, obtaining enough human annotations can be difficult in practice due to the time and efforts required. In this regard, semi-supervised learning approaches constitute a promising bypass to the label shortageMintz et al. (2009); Go et al. (2009); Ratner et al. (2016); Srivastava et al. (2017); Hancock et al. (2018). Most of these approaches foster the programmatic labeling of datasets. One such example is the Distant supervision technique Mintz et al. (2009); Go et al. (2009), 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. Another emerging technique in the natural language domain is the joint use of written explanations and labeling functions to train a generative labelling modelSrivastava et al. (2017); Hancock et al. (2018). In this context, a small subset is annotated with natural language explanations and decision labels. These, along with the instances, are then used to hot-start and parametrize the labeling functions that will be responsible for generating the labels. In our experiments, we use the distant supervision approach to exploit the already available vestiges of a legacy rule-based system in the data. We leave the exploration of other approaches for future work.
6 Conclusion and Future Work
In this work, we propose JOEL, an NN-based framework for jointly learning a decision task and associated domain knowledge explanations. Simultaneously, our framework exploits the decision-makers feedback regarding not only their decision but also the concepts that justify their decision. Initial experiments show that using human teaching can lead to significant gains in predictive accuracy and explainability. 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. 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.
The project CAMELOT (reference POCI-01-0247-FEDER-045915) leading to this work is co-financed by the ERDF - European Regional Development Fund through the Operational Program for Competitiveness and Internationalisation - COMPETE 2020, the North Portugal Regional Operational Program - NORTE 2020 and by the Portuguese Foundation for Science and Technology - FCT under the CMU Portugal international partnership.
-  (2020) Self-explaining AI as an alternative to interpretable AI. arXiv preprint arXiv:2002.05149. Cited by: §1.
-  (2019) Towards automatic concept-based explanations. In Advances in Neural Information Processing Systems, pp. 9277–9286. Cited by: §1, §1, §1, §5.
-  (2009) Twitter sentiment classification using distant supervision. CS224N project report, Stanford 1 (12), pp. 2009. Cited by: §2.3, §5.
-  (2018) Training classifiers with natural language explanations. Computing Research Repository (CoRR) abs/1805.03818. External Links: Cited by: §5.
-  (2019) Interpreting Social Respect : A Normative Lens for ML Models. External Links: Cited by: §5.
-  (2018) Interpretability beyond feature attribution: Quantitative Testing with Concept Activation Vectors (TCAV). 35th International Conference on Machine Learning, ICML 2018 6, pp. 4186–4195. External Links: Cited by: §1, §1, §1, §5.
-  (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980. Cited by: §4.
-  (2017) A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 2017-Decem (Section 2), pp. 4766–4775. External Links: Cited by: §1.
-  (2018) Towards robust interpretability with self-explaining neural networks. In Advances in Neural Information Processing Systems, pp. 7775–7784. Cited by: §1, §1, §1, §1, §2.1, §5, footnote 3.
Distant supervision for relation extraction without labeled data.
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003–1011. Cited by: §2.3, §5.
-  (2020) Doctor XAI: an ontology-based approach to black-box sequential data classification explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 629–639. Cited by: §1, §1, §5, §5.
-  (2018) Model Agnostic Supervised Local Explanations. (NeurIPS), pp. 1–10. External Links: Cited by: §1.
-  (2016) Data programming: creating large training sets, quickly. In Advances in neural information processing systems, pp. 3567–3575. Cited by: §5.
-  (2016) "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 13-17-August-2016, pp. 1135–1144. External Links: Cited by: §1, §5.
-  (2018) Anchors: High-precision model-agnostic explanations. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 1527–1535. External Links: Cited by: §1.
-  (2019) A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering. Cited by: §2.3.
-  (1986) Learning representations by back-propagating errors. Nature 323, pp. 533–536. Cited by: §2.1.
-  (2014) Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15 (1), pp. 1929–1958. Cited by: §4.
-  (2017-09) Joint concept learning and semantic parsing from natural language explanations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp. 1527–1536. External Links: Cited by: §5.
-  (2019) DLIME : A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems. External Links: Cited by: §1.
Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of the 2015 conference on empirical methods in natural language processing, pp. 1753–1762. Cited by: §2.3.
-  (2018) Multiple sclerosis identification by convolutional neural network with dropout and parametric relu. Journal of computational science 28, pp. 1–10. Cited by: §4.