Auswahl der wissenschaftlichen Literatur zum Thema „IA explicable“
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Zeitschriftenartikel zum Thema "IA explicable"
Krasnov, Fedor, Irina Smaznevich und Elena Baskakova. „Optimization approach to the choice of explicable methods for detecting anomalies in homogeneous text collections“. Informatics and Automation 20, Nr. 4 (03.08.2021): 869–904. http://dx.doi.org/10.15622/ia.20.4.5.
Der volle Inhalt der QuelleBerger, Alain, und Jean-Pierre Cotton. „Quel avenir pour la modélisation et la structuration dans un projet de management de la connaissance ?“ I2D - Information, données & documents 1, Nr. 1 (19.07.2023): 88–94. http://dx.doi.org/10.3917/i2d.231.0088.
Der volle Inhalt der QuelleVuarin, Louis, Pedro Gomes Lopes und David Massé. „L’intelligence artificielle peut-elle être une innovation responsable ?“ Innovations N° 72, Nr. 3 (29.08.2023): 103–47. http://dx.doi.org/10.3917/inno.pr2.0153.
Der volle Inhalt der QuellePérez-Salgado, Diana, María Sandra Compean-Dardón und Luis Ortiz-Hernández. „Inseguridad alimentaria y adherencia al tratamiento antirretroviral en personas con VIH de México“. Ciência & Saúde Coletiva 22, Nr. 2 (Februar 2017): 543–51. http://dx.doi.org/10.1590/1413-81232017222.10792016.
Der volle Inhalt der QuelleReus-Smit, Christian, und Ayşe Zarakol. „Polymorphic justice and the crisis of international order“. International Affairs 99, Nr. 1 (09.01.2023): 1–22. http://dx.doi.org/10.1093/ia/iiac232.
Der volle Inhalt der QuelleForster, Timon. „Respected individuals: when state representatives wield outsize influence in international organizations“. International Affairs 100, Nr. 1 (08.01.2024): 261–81. http://dx.doi.org/10.1093/ia/iiad226.
Der volle Inhalt der QuelleSowers, Jeannie, und Erika Weinthal. „Humanitarian challenges and the targeting of civilian infrastructure in the Yemen war“. International Affairs 97, Nr. 1 (Januar 2021): 157–77. http://dx.doi.org/10.1093/ia/iiaa166.
Der volle Inhalt der QuelleRenshaw, Phil St John, Emma Parry und Michael Dickmann. „International assignments – extending an organizational value framework“. Journal of Global Mobility: The Home of Expatriate Management Research 8, Nr. 2 (03.06.2020): 141–60. http://dx.doi.org/10.1108/jgm-12-2019-0055.
Der volle Inhalt der QuelleSánchez-Urán Azaña, María Yolanda. „Robótica Inclusiva: rendimiento económico y empleo“. Arbor 197, Nr. 802 (30.12.2021): a626. http://dx.doi.org/10.3989/arbor.2021.802004.
Der volle Inhalt der QuelleSYAMSUL AZIZUL MARINSAH, ABDUL HAIR BEDDU ASIS und MOHAMMAD FIRDAUS BIN HASMIN. „AMALAN SOGIT DALAM KALANGAN MASYARAKAT DUSUN DI RANAU, SABAH: ANALISIS DARI PERSPEKTIF SOSIOBUDAYA DAN HUKUM ISLAM“. MANU Jurnal Pusat Penataran Ilmu dan Bahasa (PPIB) 33, Nr. 2 (04.01.2023): 105–28. http://dx.doi.org/10.51200/manu.v33i2.2149.
Der volle Inhalt der QuelleDissertationen zum Thema "IA explicable"
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.
Der volle Inhalt der QuelleSmart homes are Cyber-Physical Systems where various components cooperate to fulfill high-level goals such as user comfort or safety. These autonomic systems can adapt at runtime without requiring human intervention. This adaptation is hard to understand for the occupant, which can hinder the adoption of smart home systems. Since the mid 2010s, explainable AI has been a topic of interest, aiming to open the black box of complex AI models. The difficulty to explain autonomic systems does not come from the intrinsic complexity of their components, but rather from their self-adaptation capability which leads changes of configuration, logic or goals at runtime. In addition, the diversity of smart home devices makes the task harder. To tackle this challenge, we propose to add an explanatory system to the existing smart home autonomic system, whose task is to observe the various controllers and devices to generate explanations. We define six goals for such a system. 1) To generate contrastive explanations in unexpected or unwanted situations. 2) To generate a shallow reasoning, whose different elements are causaly closely related to each other. 3) To be transparent, i.e. to expose its entire reasoning and which components are involved. 4) To be self-aware, integrating its reflective knowledge into the explanation. 5) To be generic and able to adapt to diverse components and system architectures. 6) To preserve privacy and favor locality of reasoning. Our proposed solution is an explanatory system in which a central component, name the ``Spotlight'', implements an algorithm named D-CAS. This algorithm identifies three elements in an explanatory process: conflict detection via observation interpretation, conflict propagation via abductive inference and simulation of possible consequences. All three steps are performed locally, by Local Explanatory Components which are sequentially interrogated by the Spotlight. Each Local Component is paired to an autonomic device or controller and act as an expert in the related knowledge domain. This organization enables the addition of new components, integrating their knowledge into the general system without need for reconfiguration. We illustrate this architecture and algorithm in a proof-of-concept demonstrator that generates explanations in typical use cases. We design Local Explanatory Components to be generic platforms that can be specialized by the addition of modules with predefined interfaces. This modularity enables the integration of various techniques for abduction, interpretation and simulation. Our system aims to handle unusual situations in which data may be scarce, making past occurrence-based abduction methods inoperable. We propose a novel approach: to estimate events memorability and use them as relevant hypotheses to a surprising phenomenon. Our high-level approach to explainability aims to be generic and paves the way towards systems integrating more advanced modules, guaranteeing smart home explainability. The overall method can also be used for other Cyber-Physical Systems
Ayats, H. Ambre. „Construction de graphes de connaissances à partir de textes avec une intelligence artificielle explicable et centrée-utilisateur·ice“. Electronic Thesis or Diss., Université de Rennes (2023-....), 2023. http://www.theses.fr/2023URENS095.
Der volle Inhalt der QuelleWith recent advances in artificial intelligence, the question of human control has become central. Today, this involves both research into explainability and designs centered around interaction with the user. What's more, with the expansion of the semantic web and automatic natural language processing methods, the task of constructing knowledge graphs from texts has become an important issue. This thesis presents a user-centered system for the construction of knowledge graphs from texts. This thesis presents several contributions. First, we introduce a user-centered workflow for the aforementioned task, having the property of progressively automating the user's actions while leaving them a fine-grained control over the outcome. Next, we present our contributions in the field of formal concept analysis, used to design an explainable instance-based learning module for relation classification. Finally, we present our contributions in the field of relation extraction, and how these fit into the presented workflow
Afchar, Darius. „Interpretable Music Recommender Systems“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS608.
Der volle Inhalt der Quelle‘‘Why do they keep recommending me this music track?’’ ‘‘Why did our system recommend these tracks to users?’’ Nowadays, streaming platforms are the most common way to listen to recorded music. Still, music recommendations — at the heart of these platforms — are not an easy feat. Sometimes, both users and engineers may be equally puzzled about the behaviour of a music recommendation system (MRS). MRS have been successfully employed to help explore catalogues that may be as large as tens of millions of music tracks. Built and optimised for accuracy, real-world MRS often end up being quite complex. They may further rely on a range of interconnected modules that, for instance, analyse audio signals, retrieve metadata about albums and artists, collect and aggregate user feedbacks on the music service, and compute item similarities with collaborative filtering. All this complexity hinders the ability to explain recommendations and, more broadly, explain the system. Yet, explanations are essential for users to foster a long-term engagement with a system that they can understand (and forgive), and for system owners to rationalise failures and improve said system. Interpretability may also be needed to check the fairness of a decision or can be framed as a means to control the recommendations better. Moreover, we could also recursively question: Why does an explanation method explain in a certain way? Is this explanation relevant? What could be a better explanation? All these questions relate to the interpretability of MRSs. In the first half of this thesis, we explore the many flavours that interpretability can have in various recommendation tasks. Indeed, since there is not just one recommendation task but many (e.g., sequential recommendation, playlist continuation, artist similarity), as well as many angles through which music may be represented and processed (e.g., metadata, audio signals, embeddings computed from listening patterns), there are as many settings that require specific adjustments to make explanations relevant. A topic like this one can never be exhaustively addressed. This study was guided along some of the mentioned modalities of musical objects: interpreting implicit user logs, item features, audio signals and similarity embeddings. Our contribution includes several novel methods for eXplainable Artificial Intelligence (XAI) and several theoretical results, shedding new light on our understanding of past methods. Nevertheless, similar to how recommendations may not be interpretable, explanations about them may themselves lack interpretability and justifications. Therefore, in the second half of this thesis, we found it essential to take a step back from the rationale of ML and try to address a (perhaps surprisingly) understudied question in XAI: ‘‘What is interpretability?’’ Introducing concepts from philosophy and social sciences, we stress that there is a misalignment in the way explanations from XAI are generated and unfold versus how humans actually explain. We highlight that current research tends to rely too much on intuitions or hasty reduction of complex realities into convenient mathematical terms, which leads to the canonisation of assumptions into questionable standards (e.g., sparsity entails interpretability). We have treated this part as a comprehensive tutorial addressed to ML researchers to better ground their knowledge of explanations with a precise vocabulary and a broader perspective. We provide practical advice and highlight less popular branches of XAI better aligned with human cognition. Of course, we also reflect back and recontextualise our methods proposed in the previous part. Overall, this enables us to formulate some perspective for our field of XAI as a whole, including its more critical and promising next steps as well as its shortcomings to overcome
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.
Der volle Inhalt der QuelleThe widespread use of high throughput technologies in the biomedical field is producing massive amounts of data, notably the new generation of genome sequencing technologies. Multiple Sequence Alignment (MSA) serves as a fundamental tool for the analysis of this data, with applications including genome annotation, protein structure and function prediction, or understanding evolutionary relationships, etc. However, the accuracy of MSA is often compromised due to factors such as unreliable alignment algorithms, inaccurate gene prediction, or incomplete genome sequencing. This thesis addresses the issue of data quality assessment by leveraging deep learning techniques. We propose novel models based on convolutional neural networks for the identification of errors in visual representations of MSAs. Our primary objective is to assist domain experts in their research studies, where the accuracy of MSAs is crucial. Therefore, we focused on providing reliable explanations for our model predictions by harnessing the potential of explainable artificial intelligence (XAI). Particularly, we leveraged visual explanations as a foundation for a transfer learning framework that aims essentially to improve a model's ability to focus on underlying features in an input. Finally, we proposed novel evaluation metrics designed to assess this ability. Initial findings suggest that our approach achieves a good balance between model complexity, performance, and explainability, and could be leveraged in domains where data availability is limited and the need for comprehensive result explanation is paramount
Buchteile zum Thema "IA explicable"
BAILLARGEAT, Dominique. „Intelligence Artificielle et villes intelligentes“. In Algorithmes et Société, 37–46. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4544.
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