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.

/pdf/landslide-inventory-maps-new-tools-for-an-old-problem-wkxin2jqxg.pdf

Landslide inventory maps: New tools for an old problem

Determination of collapse potential of collapsible grounds is an important problem for civil engineers. However, this requires extensive field and laboratory works. For this reason, prediction tools for this purpose are highly attractive for engineers. Considering this difficulty, development of a Mamdani fuzzy inference system for prediction of collapse index is the main purpose of the study. The fuzzy inference system developed in the study includes two inputs, one output and 25 lingusitic if-then rules. The performace of the fuzzy inference system is checked by various indices and these indices reveal that the fuzzy inference system has a significant prediction performance.

Prediction of the Collapse Index by a Mamdani Fuzzy Inference System

/pdf/landslide-susceptibility-mapping-of-a-landslide-prone-area-a859a89uoj.pdf

Landslide susceptibility mapping of a landslide-prone area by data mining

Evaluation of collapse mechanism and portal interaction of a High-Speed Railway Tunnel (T29 Tunnel, Turkey)

Prediction of the Uniaxial Compressive Strength of a Greywacke by Fuzzy Inference System

Expert-based landslide susceptibility modelling by using hierarchical fuzzy systems (HFS): an investigation from central part of Rif Mountains (Morocco)

Candan Gokceoglu

Papers

Prediction of the Collapse Index by a Mamdani Fuzzy Inference System

Landslide susceptibility mapping of a landslide-prone area by data mining

Evaluation of collapse mechanism and portal interaction of a High-Speed Railway Tunnel (T29 Tunnel, Turkey)

Prediction of the Uniaxial Compressive Strength of a Greywacke by Fuzzy Inference System

Expert-based landslide susceptibility modelling by using hierarchical fuzzy systems (HFS): an investigation from central part of Rif Mountains (Morocco)