Explainable AI: A review of applications to neuroimaging data

Deep neural networks (DNNs) have transformed the field of computer vision and currently constitute some of the best models for representations learned via hierarchical processing in the human brain. In medical imaging, these models have shown human-level performance and even higher in the early diagnosis of a wide range of diseases. However, the goal is often not only to accurately predict group membership or diagnose but also to provide explanations that support the model decision in a context that a human can readily interpret. The limited transparency has hindered the adoption of DNN algorithms across many domains. Numerous explainable artificial intelligence (XAI) techniques have been developed to peer inside the “black box” and make sense of DNN models, taking somewhat divergent approaches. Here, we suggest that these methods may be considered in light of the interpretation goal, including functional or mechanistic interpretations, developing archetypal class instances, or assessing the relevance of certain features or mappings on a trained model in a post-hoc capacity. We then focus on reviewing recent applications of post-hoc relevance techniques as applied to neuroimaging data. Moreover, this article suggests a method for comparing the reliability of XAI methods, especially in deep neural networks, along with their advantages and pitfalls

Comparing the Quality and Speed of Sentence Classification with Modern Language Models

After the advent of Glove and Word2vec, the dynamic development of language models (LMs) used to generate word embeddings has enabled the creation of better text classifier frameworks. With the vector representations of words generated by newer LMs, embeddings are no longer static but are context-aware. However, the quality of results provided by state-of-the-art LMs comes at the price of speed. Our goal was to present a benchmark to provide insight into the speed–quality trade-off of a sentence classifier framework based on word embeddings provided by selected LMs. We used a recurrent neural network with gated recurrent units to create sentence-level vector representations from word embeddings provided by an LM and a single fully connected layer for classification. Benchmarking was performed on two sentence classification data sets: The Sixth Text REtrieval Conference (TREC6)set and a 1000-sentence data set of our design. Our Monte Carlo cross-validated results based on these two data sources demonstrated that the newest deep learning LMs provided improvements over Glove and FastText in terms of weighted Matthews correlation coefficient (MCC) scores. We postulate that progress in LMs is more apparent when more difficult classification tasks are addressed

Predicting the Volume of Response to Tweets Posted by a Single Twitter Account

Social media users, including organizations, often struggle to acquire the maximum number of responses from other users, but predicting the responses that a post will receive before publication is highly desirable. Previous studies have analyzed why a given tweet may become more popular than others, and have used a variety of models trained to predict the response that a given tweet will receive. The present research addresses the prediction of response measures available on Twitter, including likes, replies and retweets. Data from a single publisher, the official US Navy Twitter account, were used to develop a feature-based model derived from structured tweet-related data. Most importantly, a deep learning feature extraction approach for analyzing unstructured tweet text was applied. A classification task with three classes, representing low, moderate and high responses to tweets, was defined and addressed using four machine learning classifiers. All proposed models were symmetrically trained in a fivefold cross-validation regime using various feature configurations, which allowed for the methodically sound comparison of prediction approaches. The best models achieved F1 scores of 0.655. Our study also used SHapley Additive exPlanations (SHAP) to demonstrate limitations in the research on explainable AI methods involving Deep Learning Language Modeling in NLP. We conclude that model performance can be significantly improved by leveraging additional information from the images and links included in tweets

Text Guide: Improving the quality of long text classification by a text selection method based on feature importance

The performance of text classification methods has improved greatly over the last decade for text instances of less than 512 tokens. This limit has been adopted by most state-of-the-research transformer models due to the high computational cost of analyzing longer text instances. To mitigate this problem and to improve classification for longer texts, researchers have sought to resolve the underlying causes of the computational cost and have proposed optimizations for the attention mechanism, which is the key element of every transformer model. In our study, we are not pursuing the ultimate goal of long text classification, i.e., the ability to analyze entire text instances at one time while preserving high performance at a reasonable computational cost. Instead, we propose a text truncation method called Text Guide, in which the original text length is reduced to a predefined limit in a manner that improves performance over naive and semi-naive approaches while preserving low computational costs. Text Guide benefits from the concept of feature importance, a notion from the explainable artificial intelligence domain. We demonstrate that Text Guide can be used to improve the performance of recent language models specifically designed for long text classification, such as Longformer. Moreover, we discovered that parameter optimization is the key to Text Guide performance and must be conducted before the method is deployed. Future experiments may reveal additional benefits provided by this new method.Comment: This is the reviewed and accepted for publication version of the article by the IEEE Access Journal. One of the important modifications is publication of the code along with the paper. The code can be used to apply Text Guide to a data set of ones choice. The code is available at: https://github.com/krzysztoffiok/TextGuid

Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Artificial intelligence (AI)-based systems have achieved significant success in healthcare since 2016, and AI models have accomplished medical tasks, at or above the performance levels of humans. Despite these achievements, various challenges exist in the application of AI in healthcare. One of the main challenges is safety, which is related to unsafe and incorrect actions and recommendations by AI algorithms. In response to the need to address the safety challenges, this research aimed to develop a safety controlling system (SCS) framework to reduce the risk of potential healthcare-related incidents. The framework was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. The framework represents a set of attributes in different layers and can be used as a checklist in healthcare institutions with implemented AI models. Having these attributes in healthcare systems will lead to high scores in the SCS, which indicates safe application of AI models. The proposed framework provides a basis for implementing and monitoring safety legislation, identifying the risks in AI models’ activities, improving human-AI interactions, preventing incidents from occurring, and having an emergency plan for remaining risks

Automated Classification of Evidence of Respect in the Communication through Twitter

Volcanoes of hate and disrespect erupt in societies often not without fatal consequences. To address this negative phenomenon scientists struggled to understand and analyze its roots and language expressions described as hate speech. As a result, it is now possible to automatically detect and counter hate speech in textual data spreading rapidly, for example, in social media. However, recently another approach to tackling the roots of disrespect was proposed, it is based on the concept of promoting positive behavior instead of only penalizing hate and disrespect. In our study, we followed this approach and discovered that it is hard to find any textual data sets or studies discussing automatic detection regarding respectful behaviors and their textual expressions. Therefore, we decided to contribute probably one of the first human-annotated data sets which allows for supervised training of text analysis methods for automatic detection of respectful messages. By choosing a data set of tweets which already possessed sentiment annotations we were also able to discuss the correlation of sentiment and respect. Finally, we provide a comparison of recent machine and deep learning text analysis methods and their performance which allowed us to demonstrate that automatic detection of respectful messages in social media is feasible

Automated Classification of Evidence of Respect in the Communication through Twitter

Volcanoes of hate and disrespect erupt in societies often not without fatal consequences. To address this negative phenomenon scientists struggled to understand and analyze its roots and language expressions described as hate speech. As a result, it is now possible to automatically detect and counter hate speech in textual data spreading rapidly, for example, in social media. However, recently another approach to tackling the roots of disrespect was proposed, it is based on the concept of promoting positive behavior instead of only penalizing hate and disrespect. In our study, we followed this approach and discovered that it is hard to find any textual data sets or studies discussing automatic detection regarding respectful behaviors and their textual expressions. Therefore, we decided to contribute probably one of the first human-annotated data sets which allows for supervised training of text analysis methods for automatic detection of respectful messages. By choosing a data set of tweets which already possessed sentiment annotations we were also able to discuss the correlation of sentiment and respect. Finally, we provide a comparison of recent machine and deep learning text analysis methods and their performance which allowed us to demonstrate that automatic detection of respectful messages in social media is feasible