Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions (TED), that provides meaningful explanations that match the mental model of the consumer. We illustrate the generality and effectiveness of this approach with two different examples, resulting in highly accurate explanations with no loss of prediction accuracy for these two examples.
Leveraging rationales'', explanations'', attributes'', or other privileged information'' in the training data to help improve the accuracy of the algorithms (e.g., @cite_38 @cite_32 @cite_19 @cite_15 @cite_28 @cite_26 @cite_24 @cite_22
@cite_38 @cite_26 @cite_22 @cite_28 @cite_32 @cite_24 @cite_19 @cite_15
2162096170 2153332911 2061458158 2883512601
We introduce a novel approach to incorporating domain knowledge into Support Vector Machines to improve their example efficiency. Domain knowledge is used in an Explanation Based Learning fashion to build justifications or explanations for why the training examples are assigned their given class labels. Explanations bias the large margin classifier through the interaction of training examples and domain knowledge. We develop a new learning algorithm for this Explanation-Augmented SVM (EA-SVM). It naturally extends to imperfect knowledge, a stumbling block to conventional EBL. Experimental results confirm desirable properties predicted by the analysis and demonstrate the approach on three domains. We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: annotations with “rationales.” When annotating an example, the human teacher will also highlight evidence supporting this annotation—thereby teaching the machine learner why the example belongs to the category. We provide some rationale-annotated data and present a learning method that exploits the rationales during training to boost performance significantly on a sample task, namely sentiment classification of movie reviews. We hypothesize that in some situations, providing rationales is a more fruitful use of an annotator’s time than annotating more examples. In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a web mining problem, i.e. web index recommendation, is investigated from a multi-instance view. In detail, each web index page is regarded as a bag, while each of its linked pages is regarded as an instance. A user favoring an index page means that he or she is interested in at least one page linked by the index. Based on the browsing history of the user, recommendation could be provided for unseen index pages. An algorithm named Fretcit-kNN, which employs the Minimal Hausdorff distance between frequent term sets and utilizes both the references and citers of an unseen bag in determining its label, is proposed to solve the problem. Experiments show that in average the recommendation accuracy of Fretcit-kNN is 81.0 with 71.7 recall and 70.9 precision, which is significantly better than the best algorithm that does not consider the specific characteristics of multi-instance learning, whose performance is 76.3 accuracy with 63.4 recall and 66.1 precision. Existing visual explanation generating agents learn to fluently justify a class prediction. However, they may mention visual attributes which reflect a strong class prior, although the evidence may not actually be in the image. This is particularly concerning as ultimately such agents fail in building trust with human users. To overcome this limitation, we propose a phrase-critic model to refine generated candidate explanations augmented with flipped phrases which we use as negative examples while training. At inference time, our phrase-critic model takes an image and a candidate explanation as input and outputs a score indicating how well the candidate explanation is grounded in the image. Our explainable AI agent is capable of providing counter arguments for an alternative prediction, i.e. counterfactuals, along with explanations that justify the correct classification decisions. Our model improves the textual explanation quality of fine-grained classification decisions on the CUB dataset by mentioning phrases that are grounded in the image. Moreover, on the FOIL tasks, our agent detects when there is a mistake in the sentence, grounds the incorrect phrase and corrects it significantly better than other models.