1,720,955 research outputs found
An objective metric for Explainable AI: How and why to estimate the degree of explainability
Full text linkThis paper presents a new method for objectively measuring the explainability of textual information, such as the outputs of Explainable AI (XAI). We introduce a metric called Degree of Explainability (DoX), drawing inspiration from Ordinary Language Philosophy and Achinstein's theory of explanations. It assumes that the degree of explainability is directly proportional to the number of relevant questions that a piece of information can correctly answer. We have operationalized this concept by formalizing the DoX metric through a mathematical formula, which we have integrated into a software tool named DoXpy. DoXpy relies on pre-trained deep language models for knowledge extraction and answer retrieval in order to estimate the DoX, transforming our theoretical insights into a practical tool for real-world applications. To confirm the effectiveness and consistency of our approach, we conducted comprehensive experiments and user studies with over 190 participants. These studies evaluated the quality of explanations by healthcare and finance XAI-based software systems. Our results demonstrate a correlation between increases in objective explanation usability and increments in the DoX score. These findings suggest that the DoX metric is congruent with other mainstream explainability measures. It provides a more objective and cost-effective alternative to non-deterministic user studies. Thus, we discuss the potential of DoX as a tool to evaluate the legal compliance of XAI systems. By bridging the gap between theory and practice in Explainable AI, our work fosters transparency, understandability, and legal compliance. DoXpy and related materials have been made available online to ensure reproducibility. & COPY; 2023 Elsevier B.V. All rights reserved
Perlocution vs Illocution: How Different Interpretations of the Act of Explaining Impact on the Evaluation of Explanations and XAI
Full text linkThis article discusses the concepts of illocutionary, perlocutionary, and locutionary acts, and their role in understanding explanations. Illocutionary acts concern the speaker's intended meaning, perlocutionary acts refer to the listener’s reaction, and locutionary acts are about the speech act itself. We suggest a new way to categorise established definitions of explanation based on these speech act principles. This method enhances our grasp of how explanations work. We found that if you define explanation as a perlocutionary act, it requires subjective judgements. This makes it hard to assess an explanation objectively before the listener receives it. On the other hand, we claim that existing legal systems prefer explanations based on illocutionary acts. We propose that the exact meaning of explanation depends on the situation. Some kinds of definitions suit specific circumstances better. For example, in educational settings, a perlocutionary approach often works best, while legal settings call for an illocutionary approach. Additionally, we show how current measures of explainability can be grouped based on their theoretical support and the speech act they rely on. This categorisation helps us pinpoint which measures are best for assessing the results of Explainable AI (XAI) tools in legal or other settings. In simpler terms, we are explaining how to evaluate and improve XAI and explanations in different situations, such as in education and law. By understanding where and when to apply different explainability measures, we can make better and more specialised XAI tools. This will lead to significant improvements in AI explainability
Combining shallow and deep learning approaches against data scarcity in legal domains
Full text linkWe are recently witnessing a radical shift towards digitisation in many aspects of our daily life, including law, public administration and governance. This has sometimes been done with the aim of reducing costs and human errors by improving data analysis and management, but not without raising major technological challenges. One of these challenges is certainly the need to cope with relatively small amounts of data, without sacrificing performance. Indeed, cutting-edge approaches to (natural) language processing and understanding are often data-hungry, especially those based on deep learning. With this paper we seek to address the problem of data scarcity in automatic Legalese (or legal English) processing and understanding. What we propose is an ensemble of shallow and deep learning techniques called SyntagmTuner, designed to combine the accuracy of deep learning with the ability of shallow learning to work with little data. Our contribution is based on the assumption that Legalese differs from its spoken language in the way the meaning is encoded by the structure of the text and the co-occurrence of words. As result, we show with SyntagmTuner how we can perform important tasks for e-governance, as multi-label classification of the United Nations General Assembly (UNGA) Resolutions or legal question answering, with data-sets of roughly 100 samples or even less
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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