1,720,987 research outputs found
Visual Analytics for Explainable and Trustworthy Machine Learning
No full textThe deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. While automated methods can partly handle these problems, recent research findings suggest that their combination with innovative methods developed within information visualization and visual analytics can lead to further insights gained from models and, consequently, improve their predictive ability and enhance trustworthiness in the entire process. Visual analytics is the area of research that studies the analysis of vast and intricate information spaces by combining statistical and machine learning models with interactive visual interfaces. By following this methodology, human experts can better understand such spaces and apply their domain expertise in the process of building and improving the underlying models. The primary goals of this dissertation are twofold, focusing on (1) methodological aspects, by conducting qualitative and quantitative meta-analyses to support the visualization research community in making sense of its literature and to highlight unsolved challenges, as well as (2) technical solutions, by developing visual analytics approaches for various machine learning models, such as dimensionality reduction and ensemble learning methods. Regarding the first goal, we define, categorize, and examine in depth the means for visual coverage of the different trust levels at each stage of a typical machine learning pipeline and establish a design space for novel visualizations in the area. Regarding the second goal, we discuss multiple visual analytics tools and systems implemented by us to facilitate the underlying research on the various stages of the machine learning pipeline, i.e., data processing, feature engineering, hyperparameter tuning, understanding, debugging, refining, and comparing models. Our approaches are data-agnostic, but mainly target tabular data with meaningful attributes in diverse domains, such as health care and finance. The applicability and effectiveness of this work were validated with case studies, usage scenarios, expert interviews, user studies, and critical discussions of limitations and alternative designs. The results of this dissertation provide new avenues for visual analytics research in explainable and trustworthy machine learning.Användningen av artificiell intelligens och maskininlärning har exploderat i popularitet de senaste åren, med många olika typer av modeller för att tolka och förutse mönster och trender i data från olika områden. Ju mer komplexa dessa modeller blir, desto vanligare är det att de behandlas som “svarta lådor” vilka inte medger någon insyn i hur ett visst utfall har beräknats. Detta gör det svårt för användare att utvärdera och lita på resultaten, vilket i sin tur försvårar användning i situationer där beslut av vikt ska fattas. Även om automatiserade metoder delvis kan hantera denna problematik, tyder de senaste forskningsresultaten på att dessa också bör kombineras med innovativa metoder inom informationsvisualisering och visuell analys för att ge bästa effekt. Denna kombination kan ge fördjupade insikter som kan användas för att förbättra modellernas förmåga samt för att öka tillförlitligheten i, och förtroendet för, den övergripande processen. Inom forskningsområdet visuell analys kombineras statistiska modeller och maskininlärning med interaktiva visuella gränssnitt, vilket möjliggör för domänexperter att analysera stora och komplexa datamängder, samt ger dem möjlighet att använda sina expertkunskaper för att utveckla och förbättra de underliggande modellerna. De två huvudmålen för denna avhandling är att: (1) fokusera på metodologiska aspekter genom kvalitativa och kvantitativa metaanalyser i syfte att hjälpa forskare inom området att överblicka existerande litteratur och i syfte att lyfta fram kvarvarande utmaningar, samt (2) fokusera på tekniska lösningar genom att utveckla visuella analysmetoder för olika maskininlärningsmodeller, såsom dimensionsreducering och ensembleinlärning. För att uppnå det första målet definierar, kategoriserar och detaljgranskar vi former för visuell representation av tillförlitlighet i existerande maskininlärningsramverk, och utifrån detta formulerar vi riktlinjer för design av nya visualiseringar inom området. För att uppnå det andra målet diskuterar vi flera av våra egenutvecklade visuella analysverktyg och system, som utvecklats i syfte att möjliggöra specifik forskning på de olika stegen i ett generellt maskininlärningsramverk (vilket typiskt består av: databehandling, dataförädling, inställning av parametrar, förståelse, felsökning, förbättring, samt jämförelse av olika modeller). Våra metoder kan appliceras på många olika typer av data, men riktar sig främst mot data i tabellformat från områden såsom hälsovård och finans. Tillämplighet och relevans har validerats med hjälp av fallstudier, användningsfall, intervjuer med experter, användarstudier och diskussioner rörande begränsningar och möjliga alternativa designlösningar. Innehållet i denna avhandling öppnar upp nya inriktningar för forskning i visuell analys inom förklarlig och pålitlig maskininlärning
Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework
Full text linkAI prevails in financial fraud detection and decision making. Yet, due to concerns about biased automated decision making or profiling, regulations mandate that final decisions are made by humans. Financial fraud investigators face the challenge of manually synthesizing vast amounts of unstructured information, including AI alerts, transaction histories, social media insights, and governmental laws. Current Visual Analytics (VA) systems primarily support isolated aspects of this process, such as explaining binary AI alerts and visualizing transaction patterns, thus adding yet another layer of information to the overall complexity. In this work, we propose a framework where the VA system supports decision makers throughout all stages of financial fraud investigation, including data collection, information synthesis, and human criteria iteration. We illustrate how VA can claim a central role in AI-aided decision making, ensuring that human judgment remains in control while minimizing potential biases and labor-intensive tasks.Accepted poster at IEEE VIS \u2724, Florida, USA, 13-18 October, 202
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
