MeSH (Medical Subject Headings) is a hierarchically structured thesaurus used for indexing biomedical information. This vocabulary contains most of the biomedical knowledge available to date. To keep up with the continuous evolution and expanding of our understanding on the medical field, yearly revisions take place in MeSH. These revisions introduce new descriptors in the thesaurus, in addition to changes in already existing ones, either directly or indirectly. This constant evolution of the thesaurus causes many older descriptors to exhibit some form of drift in their meaning, which in turn affects the performance of Machine Learning models trained on an older version of the thesaurus when used to predict data obtained from more recent versions. In this paper, we study the phenomenon of concept drift in MeSH, through evaluating the performance of a state-of-the-art text classification algorithm in articles from different years. We also investigate how changes in descriptors indirectly affect different ones that are related to them by studying the shifts in their co-occurrence, using this shift as a measure of concept drift.

Keyphrase extraction is a widely discussed topic in Natural Language Processing, as it offers a concise summary of the main topics in a document. Interpretability is also an important aspect in Machine Learning as it helps prevent socio-ethical issues, such as bias and discrimination against minorities, or mistakes that may have serious consequences. Interpretability has recently gained prominence in the field of Natural Language Processing, where transformers are the dominant architectures. The goal of interpretability is to provide interpretations that pinpoint the elements of an instance contributing the most to its decision. In this work, we use keyphrase extraction to facilitate the interpretability process, producing smaller, more concise interpretations that also consider word interactions, as keyphrases usually consist of multiple words. Additionally, our technique is based on semantic similarity, making it faster and zero-shot ready, which is ideal for online learning scenarios. We evaluated the effectiveness of our technique through a series of quantitative and qualitative experiments on the well-known BERT model, comparing it against several state-of-the-art competitors.

Abstract Automated Machine Learning-based systems’ integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to employing ML-based systems, if they are not interpretable, they should not be used in critical or high-risk applications. To address this issue, researchers and businesses have been focusing on finding ways to improve the explainability of complex ML systems, and several such methods have been developed. Indeed, there are so many developed techniques that it is difficult for practitioners to choose the best among them for their applications, even when using evaluation metrics. As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent. In this paper, we present a local meta-explanation technique which builds on top of the truthfulness metric, which is a faithfulness-based metric. We demonstrate the effectiveness of both the technique and the metric by concretely defining all the concepts and through experimentation.

The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.

Hatebusters is a web application for actively reporting YouTube hate speech, aiming to establish an online community of volunteer citizens. Hatebusters searches YouTube for videos with potentially hateful comments, scores their comments with a classifier trained on human-annotated data and presents users those comments with the highest probability of being hate speech. It also employs gamification elements, such as achievements and leaderboards, to drive user engagement.