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Dissertations / Theses on the topic 'Explainable AI'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

PASTOR, ELIANA. "Pattern-based algorithms for Explainable AI." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2942116.

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PANIGUTTI, Cecilia. "eXplainable AI for trustworthy healthcare applications." Doctoral thesis, Scuola Normale Superiore, 2022. https://hdl.handle.net/11384/125202.

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Acknowledging that AI will inevitably become a central element of clinical practice, this thesis investigates the role of eXplainable AI (XAI) techniques in developing trustworthy AI applications in healthcare. The first part of this thesis focuses on the societal, ethical, and legal aspects of the use of AI in healthcare. It first compares the different approaches to AI ethics worldwide and then focuses on the practical implications of the European ethical and legal guidelines for AI applications in healthcare. The second part of the thesis explores how XAI techniques can help meet thr

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Strineholm, Philippe. "Exploring Human-Robot Interaction Through Explainable AI Poetry Generation." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54606.

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As the field of Artificial Intelligence continues to evolve into a tool of societal impact, a need of breaking its initial boundaries as a computer science discipline arises to also include different humanistic fields. The work presented in this thesis revolves around the role that explainable artificial intelligence has in human-robot interaction through the study of poetry generators. To better understand the scope of the project, a poetry generators study presents the steps involved in the development process and the evaluation methods. In the algorithmic development of poetry generators, t

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Stiff, Harald. "Explainable AI as a Defence Mechanism for Adversarial Examples." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260347.

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Deep learning is the gold standard for image classification tasks. With its introduction came many impressive improvements in computer vision outperforming all of the earlier machine learning models. However, in contrast to the success it has been shown that deep neural networks are easily fooled by adversarial examples, data that have been modified slightly to cause the neural networks to make incorrect classifications. This significant disadvantage has caused an increased doubt in neural networks and it has been questioned whether or not they are safe to use in practice. In this thesis we pr

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FRACCAROLI, MICHELE. "Explainable Deep Learning." Doctoral thesis, Università degli studi di Ferrara, 2023. https://hdl.handle.net/11392/2503729.

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Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelli

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Vincenzi, Leonardo. "eXplainable Artificial Intelligence User Experience: contesto e stato dell’arte." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23338/.

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Il grande sviluppo del mondo dell’Intelligenza Artificiale unito alla sua vastissima applicazione in molteplici ambiti degli ultimi anni, ha portato a una sempre maggior richiesta di spiegabilità dei sistemi di Machine Learning. A seguito di questa necessità il campo dell’eXplainable Artificial Intelligence ha compiuto passi importanti verso la creazione di sistemi e metodi per rendere i sistemi intelligenti sempre più trasparenti e in un futuro prossimo, per garantire sempre più equità e sicurezza nelle decisioni prese dall’AI, si prevede una sempre più rigida regolamentazione verso la sua sp

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Benedetti, Riccardo. "Neuroimaging e disturbo dello spettro autistico: classificazione con approccio explainable AI." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Il disturbo dello spettro autistico (Autistic Spectrum Disorder - ASD) indica un ventaglio di diagnosi che vanno dalla Sindrome di Asperger all'autismo e che sono accumunate dalla presenza di sintomi comuni, che compromettono l'aspetto comportamentale e i rapporti con la società del soggetto. Al momento la diagnosi di ASD avviene affidandosi a test standardizzati riconosciuti eseguiti da personale medico specializzato. Negli ultimi anni si sono però generati diversi dataset di neuroimaging in cui vengono raccolte le immagini di risonanza magnetica provenienti da centri differenti e acquisite s

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Cifonelli, Antonio. "Probabilistic exponential smoothing for explainable AI in the supply chain domain." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR41.

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Le rôle clé que l’IA pourrait jouer dans l’amélioration des activités commerciales est connu depuis longtemps, mais le processus de pénétration de cette nouvelle technologie a rencontré certains freins au sein des entreprises, en particulier, les coûts de mise œuvre. En moyenne, 2.8 ans sont nécessaires depuis la sélection du fournisseur jusqu’au déploiement complet d’une nouvelle solution. Trois points fondamentaux doivent être pris en compte lors du développement d’un nouveau modèle. Le désalignement des attentes, le besoin de compréhension et d’explications et les problèmes de performance e

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Guimbaud, Jean-Baptiste. "Enhancing Environmental Risk Scores with Informed Machine Learning and Explainable AI." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10188.

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Dès la conception, des facteurs environnementaux tels que la qualité de l'air ou les habitudes alimentaires peuvent significativement influencer le risque de développer diverses maladies chroniques. Dans la littérature épidémiologique, des indicateurs connus sous le nom de Scores de Risque Environnemental (Environmental Risk Score, ERS) sont utilisés non seulement pour identifier les individus à risque, mais aussi pour étudier les relations entre les facteurs environnementaux et la santé. Une limite de la plupart des ERSs est qu'ils sont exprimés sous forme de combinaisons linéaires d'un nombr

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Radulovic, Nedeljko. "Post-hoc Explainable AI for Black Box Models on Tabular Data." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT028.

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Les modèles d'intelligence artificielle (IA) actuels ont fait leurs preuves dans la résolution de diverses tâches, telles que la classification, la régression, le traitement du langage naturel (NLP) et le traitement d'images. Les ressources dont nous disposons aujourd'hui nous permettent d'entraîner des modèles d'IA très complexes pour résoudre différents problèmes dans presque tous les domaines : médecine, finance, justice, transport, prévisions, etc. Avec la popularité et l'utilisation généralisée des modèles d'IA, la nécessite d'assurer la confiance dans ces modèles s'est également accrue.

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SEVESO, ANDREA. "Symbolic Reasoning for Contrastive Explanations." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404830.

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La necessità di spiegazioni sui sistemi di Machine Learning (ML) sta crescendo man mano che i nuovi modelli superano in performance i loro predecessori, diventando più complessi e meno comprensibili per gli utenti finali. Un passaggio essenziale nella ricerca in ambito eXplainable Artificial Intelligence (XAI) è la creazione di modelli interpretabili che mirano ad approssimare la funzione decisionale di un algoritmo black box. Sebbene negli ultimi anni siano stati proposti diversi metodi di XAI, non è stata prestata sufficiente attenzione alla spiegazione di come i modelli modificano il loro

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Corinaldesi, Marianna. "Explainable AI: tassonomia e analisi di modelli spiegabili per il Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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La complessità dei modelli di Deep Learning ha permesso di ottenere risultati sbalorditivi in termini di accuratezza. Tale complessità è data sia dalla struttura non lineare e multistrato delle reti neurali profonde, sia dal loro elevato numero di parametri calcolati. Tuttavia, questo causa grandi difficoltà nello spiegare il processo decisionale di una rete neurale, che in alcuni contesti è però essenziale. Di fatto, per permettere l’accesso alle tecnologie di Deep Learning e Machine Learning anche ai settori critici - ovvero quei settori in cui le decisioni hanno un peso importante, quali l’

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Hammarström, Tobias. "Towards Explainable Decision-making Strategies of Deep Convolutional Neural Networks : An exploration into explainable AI and potential applications within cancer detection." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424779.

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The influence of Artificial Intelligence (AI) on society is increasing, with applications in highly sensitive and complicated areas. Examples include using Deep Convolutional Neural Networks within healthcare for diagnosing cancer. However, the inner workings of such models are often unknown, limiting the much-needed trust in the models. To combat this, Explainable AI (XAI) methods aim to provide explanations of the models' decision-making. Two such methods, Spectral Relevance Analysis (SpRAy) and Testing with Concept Activation Methods (TCAV), were evaluated on a deep learning model classifyi

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MARCONI, LUCA. "An eXplainable Recommender System for Course Creation in the Educational Platform “WhoTeach"." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404518.

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Secondo il World Economic Forum, entro il 2025 almeno il 50% dei lavoratori in ogni settore necessiterà di significativi processi di re-skilling e up-skilling, mirati ad aumentare o aggiornare le competenze della forza lavoro. In questo scenario complessivo, docenti, formatori ed esperti, in qualsiasi ambito educativo, necessitano di sistemi di raccomandazione, basati su intelligenza artificiale, in grado di supportarli nella creazione di corsi e percorsi di apprendimento personalizzati. I docenti hanno bisogno di tool che li aiutino a ottenere i giusti suggerimenti sulle risorse didattiche e

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Norrie, Christian. "Explainable AI techniques for sepsis diagnosis : Evaluating LIME and SHAP through a user study." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19845.

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Articial intelligence has had a large impact on many industries and transformed some domains quite radically. There is tremendous potential in applying AI to the eld of medical diagnostics. A major issue with applying these techniques to some domains is an inability for AI models to provide an explanation or justication for their predictions. This creates a problem wherein a user may not trust an AI prediction, or there are legal requirements for justifying decisions that are not met. This thesis overviews how two explainable AI techniques (Shapley Additive Explanations and Local Interpretable

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Moreno, Felipe(Felipe I. ). "Expresso-AI : a framework for explainable video based deep learning models through gestures and expressions." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130700.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 95-102).<br>We have developed a framework for Analyzing Facial Videos and applying it to Automatic Depression Detection. We also developed a video based models We have developed a framework to analyze the decisions of Deep Neural Networks trained on facial videos. We test this framework on Automatic Depression Detection. We first train Deep Convolutional Neural Networks (DCNN

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El, Qadi El Haouari Ayoub. "An EXplainable Artificial Intelligence Credit Rating System." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS486.

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Au cours des dernières années, le déﬁcit de ﬁnancement du commerce a atteint le chiﬀre alarmant de 1 500 milliards de dollars, soulignant une crise croissante dans le commerce mondial. Ce déﬁcit est particulièrement préjudiciable aux petites et moyennes entreprises (PME), qui éprouvent souvent des diﬃcultés à accéder au ﬁnancement du commerce. Les systèmes traditionnels d'évaluation du crédit, qui constituent l'épine dorsale du ﬁnance-ment du commerce, ne sont pas toujours adaptés pour évaluer correctement la solvabilité des PME. Le terme "credit scoring" désigne les méthodes et techniques uti

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Houzé, Etienne. "A generic and adaptive approach to explainable AI in autonomic systems : the case of the smart home." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT022.

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Les maisons intelligentes sont des systèmes cyber-physiques dans lesquels de nombreux composants intéragissent les uns avec les autres pour accomplir des objectifs de haut niveau comme le confort ou la sécurité de l'occupant. Ces systèmes autonomiques sont capables de s'adapter sans demander d'intervention de la part de l'utilisateur: ce fonctionnement autonomique est difficile à comprendre pour l'occupant. Ce manque d'explicabilité peut être un frein à l'adoption plus large de tels systèmes. Depuis le milieu des années 2010, l'explicabilité des modèles complexes d'IA est devenue un sujet de r

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Gjeka, Mario. "Uno strumento per le spiegazioni di sistemi di Explainable Artificial Intelligence." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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L'obiettivo di questa tesi è quello di mostrare l’importanza delle spiegazioni in un sistema intelligente. Il bisogno di avere un'intelligenza artificiale spiegabile e trasparente sta crescendo notevolmente, esigenza evidenziata dalla ricerca delle aziende di sviluppare sistemi informatici intelligenti trasparenti e spiegabili.

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Melsion, Perez Gaspar Isaac. "Leveraging Explainable Machine Learning to Raise Awareness among Preadolescents about Gender Bias in Supervised Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287554.

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Machine learning systems have become ubiquitous into our society. This has raised concerns about the potential discrimination that these systems might exert due to unconscious bias present in the data, for example regarding gender and race. Whilst this issue has been proposed as an essential subject to be included in the new AI curricula for schools, research has shown that it is a difficult topic to grasp by students. This thesis aims to develop an educational platform tailored to raise the awareness of the societal implications of gender bias in supervised learning. It assesses whether using

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REPETTO, MARCO. "Black-box supervised learning and empirical assessment: new perspectives in credit risk modeling." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/402366.

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I recenti algoritmi di apprendimento automatico ad alte prestazioni sono convincenti ma opachi, quindi spesso è difficile capire come arrivano alle loro previsioni, dando origine a problemi di interpretabilità. Questi problemi sono particolarmente rilevanti nell'apprendimento supervisionato, dove questi modelli "black-box" non sono facilmente comprensibili per le parti interessate. Un numero crescente di lavori si concentra sul rendere più interpretabili i modelli di apprendimento automatico, in particolare quelli di apprendimento profondo. Gli approcci attualmente proposti si basano su un'i

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Stensrud, Brian. "FAMTILE: AN ALGORITHM FOR LEARNING HIGH-LEVEL TACTICAL BEHAVIOR FROM OBSERVATION." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3102.

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This research focuses on the learning of a class of behaviors defined as high-level behaviors. High-level behaviors are defined here as behaviors that can be executed using a sequence of identifiable behaviors. Represented by low-level contexts, these behaviors are known a priori to learning and can be modeled separately by a knowledge engineer. The learning task, which is achieved by observing an expert within simulation, then becomes the identification and representation of the low-level context sequence executed by the expert. To learn this sequence, this research proposes FAMTILE - the Fuz

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Fall, Ahmad. "Interpretability of Neural Networks applied to Electrocardiograms : Translational Applications in Cardiovascular Diseases." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS473.pdf.

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L’électrocardiogramme (ECG) est un outil non invasif permettant d’évaluer l’activité électrique du cœur. Ils sont largement utilisés dans la détection d’anomalies cardiaques. Les algorithmes d’apprentissage profond permettent la détection automatique de schémas complexes dans les données ECG, ce qui offre un potentiel important pour l’amélioration du diagnostic médical. Toutefois, leur adoption est freinée par un faible niveau de confiance des cliniciens et un besoin massif de données pour entrainer les modèles. L’intelligence artificielle, en particulier l’apprentissage profond (deep learning

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Chromik, Michael [Verfasser], and Andreas [Akademischer Betreuer] Butz. "Human-centric explanation facilities : explainable AI for the pragmatic understanding of non-expert end users / Michael Chromik ; Betreuer: Andreas Butz." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021. http://d-nb.info/1238017088/34.

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NOBANI, NAVID. "Empowering XAI and LMI with Human-in-the-loop." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404831.

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Un tempo mirata ad imitare il cervello umano ed esistente solo come modelli matematici nel mondo accademico, la vasta famiglia dei metodi di Intelligenza Artificiale ha ben superato il suo obiettivo iniziale; ed ora comprende modelli con miliardi di parametri addestrati su milioni di dati per lo più generati dall'uomo. Tali modelli sono presenti in quasi ogni aspetto della nostra vita, dalle previsioni meteorologiche e dai contenuti di social network, da come le nostre banche rilevano transazioni fraudolente collegate ai nostri conti, a mappe che ci guidano verso un ristorante attraverso st

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Faria, Francisco Henrique Otte Vieira de. "Learning acyclic probabilistic logic programs from data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-27022018-090821/.

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To learn a probabilistic logic program is to find a set of probabilistic rules that best fits some data, in order to explain how attributes relate to one another and to predict the occurrence of new instantiations of these attributes. In this work, we focus on acyclic programs, because in this case the meaning of the program is quite transparent and easy to grasp. We propose that the learning process for a probabilistic acyclic logic program should be guided by a scoring function imported from the literature on Bayesian network learning. We suggest novel techniques that lead to orders of magni

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Attolou, Hervé-Madelein. "Explications pour des recommandations manquantes basées sur les graphes." Electronic Thesis or Diss., CY Cergy Paris Université, 2024. http://www.theses.fr/2024CYUN1337.

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Cette thèse explore le domaine spécifique des explications du type "Pourquoipas" (recommandations manquantes), qui se concentrent sur l'explication del'absence de certains éléments dans la liste de recommandations. Le besoind'explications recommandations manquantes est particulièrement crucial dans desscénarios de recommandation complexes, où l'absence de certaines recommandationspeut entraîner l'insatisfaction ou la méfiance des utilisateurs. Par exemple, un util-isateur d'une plateforme de commerce en ligne pourrait se demander pourquoi unproduit spécifique n'a pas été recommandé malgré le f

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Andersson, Filip, and Jonatan Flyckt. "Explaining rifle shooting factors through multi-sensor body tracking : Using transformers and attention to mine actionable patterns from skeleton graphs." Thesis, Jönköping University, JTH, Avdelningen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53369.

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There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but most of them have focused on single aspects of postural control. This thesis has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. We performed a data collection with 13 human participants who carried out live rifle shooting scenarios while being recorded with multiple biometric sensors, including

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VOUKELATOU, Vasiliki. "Measuring well-being through novel digital data." Doctoral thesis, Scuola Normale Superiore, 2022. https://hdl.handle.net/11384/125822.

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Well-being is an important value for people's lives, and it is crucial for societal progress. Considering that well-being is a vague and multi-dimensional concept, it cannot be captured as a whole but through a set of health, socio-economic, safety, environmental, and political dimensions. The current Ph.D. thesis focuses on the safety dimension, and in particular on peace, which is an emerging challenge nowadays. Peace is the way out of inequity and violence, and its measurement is crucial, considering that the world is constantly under socio-economic, political, and military instabilit

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Hegemann, Lena. "Reciprocal Explanations : An Explanation Technique for Human-AI Partnership in Design Ideation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281339.

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Advancements in creative artificial intelligence (AI) are leading to systems that can actively work together with designers in tasks such as ideation, i.e. the creation, development, and communication of ideas. In human group work, making suggestions and explaining the reasoning behind them as well as comprehending other group member’s explanations aids reflection, trust, alignment of goals and inspiration through diverse perspectives. Despite their ability to inspire through independent suggestions, state-of-the-art creative AI systems do not leverage these advantages of group work due to mis

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Fjellström, Lisa. "The Contribution of Visual Explanations in Forensic Investigations of Deepfake Video : An Evaluation." Thesis, Umeå universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184671.

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Videos manipulated by machine learning have rapidly increased online in the past years. So called deepfakes can depict people who never participated in a video recording by transposing their faces onto others in it. This raises the concern of authenticity of media, which demand for higher performing detection methods in forensics. Introduction of AI detectors have been of interest, but is held back today by their lack of interpretability. The objective of this thesis was therefore to examine what the explainable AI method local interpretable model-agnostic explanations (LIME) could contribute

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Holmberg, Lars. "Human In Command Machine Learning." Licentiate thesis, Malmö universitet, Malmö högskola, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42576.

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Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. The focus in this work is one of these areas; ML systems where decisions concerning ML model training, usage and selection of target domain lay in the hands of domain experts. This work is then on ML systems that function as a tool that augments and/or enhance human capabilities. The approach presented is denoted Human In Command ML

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KRAYANI, ALI. "Learning Self-Awareness Models for Physical Layer Security in Cognitive and AI-enabled Radios." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1074612.

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Cognitive Radio (CR) is a paradigm shift in wireless communications to resolve the spectrum scarcity issue with the ability to self-organize, self-plan and self-regulate. On the other hand, wireless devices that can learn from their environment can also be taught things by malicious elements of their environment, and hence, malicious attacks are a great concern in the CR, especially for physical layer security. This thesis introduces a data-driven Self-Awareness (SA) module in CR that can support the system to establish secure networks against various attacks from malicious users. Such users c

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Alabdallah, Abdallah. "Human Understandable Interpretation of Deep Neural Networks Decisions Using Generative Models." Thesis, Högskolan i Halmstad, Halmstad Embedded and Intelligent Systems Research (EIS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41035.

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Deep Neural Networks have long been considered black box systems, where their interpretability is a concern when applied in safety critical systems. In this work, a novel approach of interpreting the decisions of DNNs is proposed. The approach depends on exploiting generative models and the interpretability of their latent space. Three methods for ranking features are explored, two of which depend on sensitivity analysis, and the third one depends on Random Forest model. The Random Forest model was the most successful to rank the features, given its accuracy and inherent interpretability.

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Joel, Viklund. "Explaining the output of a black box model and a white box model: an illustrative comparison." Thesis, Uppsala universitet, Filosofiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420889.

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The thesis investigates how one should determine the appropriate transparency of an information processing system from a receiver perspective. Research in the past has suggested that the model should be maximally transparent for what is labeled as ”high stake decisions”. Instead of motivating the choice of a model’s transparency on the non-rigorous criterion that the model contributes to a high stake decision, this thesis explores an alternative method. The suggested method involves that one should let the transparency depend on how well an explanation of the model’s output satisfies the purpo

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Naqvi, Syed Muhammad Raza. "Exploration des LLM et de l'XAI sémantique pour les capacités des robots industriels et les connaissances communes en matière de fabrication." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2025. http://www.theses.fr/2025TLSEP014.

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Dans l'industrie 4.0, la fabrication avancée est essentielle pour façonner les usines du futur, en permettant d'améliorer la planification, l'ordonnancement et le contrôle. La capacité d'adapter rapidement les lignes de production en réponse aux demandes des clients ou à des situations inattendues est essentielle pour améliorer l'avenir de la fabrication. Bien que l'IA apparaisse comme une solution, les industries s'appuient toujours sur l'expertise humaine en raison des problèmes de confiance et du manque de transparence des décisions de l'IA. L'IA explicable intégrant des connaissances de ba

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Gusmão, Arthur Colombini. "Interpreting embedding models of knowledge bases." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-04022019-094854/.

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Knowledge bases are employed in a variety of applications, from natural language processing to semantic web search; alas, in practice, their usefulness is hurt by their incompleteness. To address this issue, several techniques aim at performing knowledge base completion, of which embedding models are efficient, attain state-of-the-art accuracy, and eliminate the need for feature engineering. However, embedding models predictions are notoriously hard to interpret. In this work, we propose model-agnostic methods that allow one to interpret embedding models by extracting weighted Horn rules from

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FUTIA, GIUSEPPE. "Neural Networks forBuilding Semantic Models and Knowledge Graphs." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850594.

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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a m

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Malik, Muhammad Hamza. "Information extraction and mapping for KG construction with learned concepts from scientic documents : Experimentation with relations data for development of concept learner." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285572.

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Systematic review of research manuscripts is a common procedure in which research studies pertaining a particular field or domain are classified and structured in a methodological way. This process involves, between other steps, an extensive review and consolidation of scientific metrics and attributes of the manuscripts, such as citations, type or venue of publication. The extraction and mapping of relevant publication data, evidently, is a very laborious task if performed manually. Automation of such systematic mapping steps intend to reduce the human effort required and therefore can potent

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Fauvel, Kevin. "Enhancing performance and explainability of multivariate time series machine learning methods : applications for social impact in dairy resource monitoring and earthquake early warning." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S043.

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Le déploiement massif de capteurs couplé à leur exploitation dans de nombreux secteurs génère une masse considérable de données multivariées qui se sont révélées clés pour la recherche scientifique, les activités des entreprises et la définition de politiques publiques. Plus spécifiquement, les données multivariées qui intègrent une évolution temporelle, c’est-à-dire des séries temporelles, ont reçu une attention toute particulière ces dernières années, notamment grâce à des applications critiques de monitoring (e.g. mobilité, santé) et l’apprentissage automatique. Cependant, pour de nombreuse

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Costa, Bueno Vicente. "Fuzzy Horn clauses in artificial intelligence: a study of free models, and applications in art painting style categorization." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673374.

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Aquesta tesi doctoral contribueix a l’estudi de les clàusules de Horn en lògiques difuses, així com al seu ús en representació difusa del coneixement aplicada al disseny d’un algorisme de classificació de pintures segons el seu estil artístic. En la primera part del treball ens centrem en algunes nocions rellevants per a la programació lògica, com ho són per exemple els models lliures i les estructures de Herbrand en lògica matemàtica difusa. Així doncs, provem l’existència de models lliures en classes universals difuses de Horn, i demostrem que tota teoria difusa universal de Horn sense igual

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Bennetot, Adrien. "A Neural-Symbolic learning framework to produce interpretable predictions for image classification." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS418.

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L'intelligence artificielle s'est développée de manière exponentielle au cours de la dernière décennie. Son évolution est principalement liée aux progrès des processeurs des cartes graphiques des ordinateurs, permettant d'accélérer le calcul des algorithmes d'apprentissage, et à l'accès à des volumes massifs de données. Ces progrès ont été principalement motivés par la recherche de modèles de prédiction de qualité, rendant ces derniers extrêmement précis mais opaques. Leur adoption à grande échelle est entravée par leur manque de transparence, ce qui provoque l'émergence de l'intelligence arti

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Bove, Clara. "Conception et évaluation d’interfaces utilisateur explicatives pour systèmes complexes en apprentissage automatique." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS247.pdf.

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Cette thèse se place dans le domaine de l’IA eXplicable (XAI) centrée sur l’humain, et plus particulièrement sur l’intelligibilité des explications pour les utilisateurs non-experts. Le contexte technique est le suivant : d’un côté, un classificateur ou un régresseur opaque fournit une prédiction, et une approche XAI post-hoc génère des informations qui agissent comme des explications ; de l’autre côté, l’ utilisateur reçoit à la fois la prédiction et ces explications. Dans ce contexte, plusieurs problèmes peuvent limiter la qualité des explications. Ceux sur lesquels nous nous concentrons son

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Lambert, Benjamin. "Quantification et caractérisation de l'incertitude de segmentation d'images médicales pardes réseaux profonds." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALS011.

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Ces dernières années, les algorithmes d'intelligence artificielle ont démontré des performances exceptionnelles dans de nombreuses tâches, incluant la segmentation et classification d'images médicales. La segmentation automatique des lésions dans des IRMs du cerveau permet une quantification rapide de la progression de la maladie : un compte des nouvelles lésions, une mesure du volume lésionnel total et une description de la forme des lésions. Cette analyse peut ensuite être exploitée par le neuro-radiologue qui peut s'en servir pour adapter le traitement thérapeutique si nécessaire. Cela perm

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Condevaux, Charles. "Méthodes d'apprentissage automatique pour l'analyse de corpus jurisprudentiels." Thesis, Nîmes, 2021. http://www.theses.fr/2021NIME0008.

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Les décisions de justice contiennent des informations déterministes (dont le contenu est récurrent d'une décision à une autre) et des informations aléatoires (à caractère probabiliste). Ces deux types d'information rentrent en ligne de compte dans la prise de décision d’un juge. Les premières peuvent la conforter dans la mesure où l’information déterministe est un élément récurrent et bien connu de la jurisprudence (i.e. des résultats d’affaires passées). Les secondes, apparentées à des caractères rares ou exceptionnels, peuvent rendre la prise de décision difficile et peuvent elles-mêmes modi

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Khodji, Hiba. "Apprentissage profond et transfert de connaissances pour la détection d'erreurs dans les séquences biologiques." Electronic Thesis or Diss., Strasbourg, 2023. http://www.theses.fr/2023STRAD058.

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L'utilisation généralisée des technologies à haut débit dans le domaine biomédical génère d'énormes quantités de données, notamment la nouvelle génération de technologies de séquençage du génome. L'alignement multiple de séquences sert d'outil fondamental pour analyser ces données, avec des applications dans l'annotation des génomes, prédiction des structures et fonctions des protéines, ou la compréhension des relations évolutives, etc. Toutefois, divers facteurs, tels que des algorithmes d'alignement peu fiables, une prédiction de gènes incorrecte, ou des séquençages génomiques incomplets, on

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Damay, Gabriel. "Dynamic Decision Trees and Community-based Graph Embeddings : towards Interpretable Machine Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT047.

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L'apprentissage automatique est le domaine des sciences informatiques dont le but est de créer des modèles et des solutions à partir de données sans savoir exactement les instructions qui dirigent intrinsèquement ces modèles. Ce domaine a obtenu des résultats impressionnants mais il est l'objet le sujet d'inquiétudes en raison notamment de l'impossibilité de comprendre et d'auditer les modèles qu'il produit. L'apprentissage automatique interprétable propose une solution à ces inquiétudes en créant des modèles qui sont interprétables de façon inhérante. Cette thèse contribue à l'apprentissage a

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Figueiredo, Luís Ramos Pinto de. "Interpreting Hierarchichal Data Features - Towards Explainable AI." Master's thesis, 2018. https://repositorio-aberto.up.pt/handle/10216/115340.

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Figueiredo, Luís Ramos Pinto de. "Interpreting Hierarchichal Data Features - Towards Explainable AI." Dissertação, 2018. https://repositorio-aberto.up.pt/handle/10216/115340.

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