Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

http://www.geoplanning.it/test/wp-content/uploads/2012/02/Practical-estimates-of-rock-mass-strength.pdf

Practical estimates of rock mass strength

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

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Landslide inventory maps: New tools for an old problem

Discontinuity controlled slope failure zoning for a granitoid complex: A fuzzy approach

Stabilization of expansive Ankara Clay with lime

This note is to point out and correct an error in Sezer et al. (2011). In the paper (Sezer et al. 2011), the authors mention “ANFIS model has not been used for landslide susceptibility mapping previously”. This statement must be corrected as “The ANFIS model has been applied in landslide susceptibility mapping previously by Pradhan, Sezer, Gokceoglu, and Buchroithner (2010) in a different study area namely Cameron Highlands, Malaysia.”

Erratum to: `Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia` [Expert Systems with Applications 38 (2011) 8208`8219]

In Turkey, landslides cause several types of damage in addition to causalities in every year. Because of the losses caused by landslides, applications to reduce damage and losses require the knowledge of current and potential spatial distribution of mass movements, called a landslide inventory. For the present study, a comprehensive landslide inventory of a landslide-prone area in Turkey, a part of Canakkale, Gallipoli Peninsula, was prepared. A total of 211 landslides were identified and mapped; most are rotational. Employing the landslide inventory, the possible causative factors of the landslides, considering their types and dimensions are discussed.

Open image in new window Landslide Inventory of the Eastern Part of the Gallipoli Peninsula (Canakkale, Turkey)

DOI 10.1007/s10346-018-01134-y Published online: 6 February 2019 © Springer-Verlag GmbH Germany part of Springer Nature 2019 Kyoji Sassa I Khang Dang I Fausto Guzzetti I Nicola Casagli I Binod Tiwari I Matjaž Mikoš I Vit Vilimek I Peter Bobrowsky I Kazuo Konagai I Željko Arbanas I Snježana Mihalić Arbanas I Ping Lu I Katsuo Sasahara I Irasema Alcantara-Ayala I Alexander Strom I Michael Hendry I Hiromitsu Yamagishi I Veronica Tofani I Sabatino Cuomo I Faisal Fathani I Jan Klimeš I Fawu Wang I Paola Reichenbach I Candan Gokceoglu I Daisuke Higaki I Tomofumi Koyama

Candan Gokceoglu

Papers

Discontinuity controlled slope failure zoning for a granitoid complex: A fuzzy approach

Stabilization of expansive Ankara Clay with lime

Erratum to: `Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia` [Expert Systems with Applications 38 (2011) 8208`8219]

Open image in new window Landslide Inventory of the Eastern Part of the Gallipoli Peninsula (Canakkale, Turkey)

Invited and accepted speakers of the Fifth World Landslide Forum in Kyoto, 2020