Jose M. Alonso

Bio: Jose M. Alonso is an academic researcher from University of Santiago de Compostela. The author has contributed to research in topics: Fuzzy logic & Fuzzy control system. The author has an hindex of 23, co-authored 105 publications receiving 2000 citations. Previous affiliations of Jose M. Alonso include University of Santiago, Chile & ETSI.

A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence

Abstract: A number of algorithms in the field of artificial intelligence offer poorly interpretable decisions. To disclose the reasoning behind such algorithms, their output can be explained by means of so-called evidence-based (or factual) explanations. Alternatively, contrastive and counterfactual explanations justify why the output of the algorithms is not any different and how it could be changed, respectively. It is of crucial importance to bridge the gap between theoretical approaches to contrastive and counterfactual explanation and the corresponding computational frameworks. In this work we conduct a systematic literature review which provides readers with a thorough and reproducible analysis of the interdisciplinary research field under study. We first examine theoretical foundations of contrastive and counterfactual accounts of explanation. Then, we report the state-of-the-art computational frameworks for contrastive and counterfactual explanation generation. In addition, we analyze how grounded such frameworks are on the insights from the inspected theoretical approaches. As a result, we highlight a variety of properties of the approaches under study and reveal a number of shortcomings thereof. Moreover, we define a taxonomy regarding both theoretical and practical approaches to contrastive and counterfactual explanation.

HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism

Abstract: This work describes a new methodology for making easier the design process of interpretable knowledge bases. It considers both expert knowledge and knowledge extracted from data. The combination of both kinds of knowledge is likely to yield robust compact systems with a good trade-off between accuracy and interpretability. Fuzzy logic offers an integration framework where both types of knowledge are represented using the same formalism. However, as two knowledge bases may convey contradictions and-or redundancies, the integration process must be made carefully. Results obtained, in four well-known benchmark classification problems, show that our methodology leads to highly interpretable knowledge bases with a good accuracy, comparable to that achieved by other methodologies. © 2008 Wiley Periodicals, Inc.

Interpretability of Fuzzy Systems: Current Research Trends and Prospects

Abstract: Fuzzy systems are universally acknowledged as valuable tools to model complex phenomena while preserving a readable form of knowledge representation. The resort to natural language for expressing the terms involved in fuzzy rules, in fact, is a key factor to conjugate mathematical formalism and logical inference with human-centered interpretability . That makes fuzzy systems specifically suitable in every real-world context where people are in charge of crucial decisions. This is because the self-explanatory nature of fuzzy rules profitably supports expert assessments. Additionally, as far as interpretability is investigated, it appears that (a) the simple adoption of fuzzy sets in modeling is not enough to ensure interpretability; (b) fuzzy knowledge representation must confront the problem of preserving the overall system accuracy, thus yielding a trade-off which is frequently debated. Such issues have attracted a growing interest in the research community and became to assume a central role in the current literature panorama of computational intelligence. This chapter gives an overview of the topics related to fuzzy system interpretability, facing the ambitious goal of proposing some answers to a number of open challenging questions: What is interpretability? Why interpretability is worth considering? How to ensure interpretability, and how to assess (quantify) it? Finally, how to design interpretable fuzzy models?

Semiconductor Composites: Strategies for Enhancing Charge Carrier Separation to Improve Photocatalytic Activity

Abstract: The formation of semiconductor composites comprising multicomponent or multiphase heterojunctions is a very effective strategy to design highly active photocatalyst systems. This review summarizes the recent strategies to develop such composites, and highlights the most recent developments in the fi eld. After a general introduction into the different strategies to improve photocatalytic activity through formation of heterojunctions, the three different types of heterojunctions are introduced in detail, followed by a historical introduction to semiconductor heterojunction systems and a thorough literature overview. Special chapters describe the highly-investigated carbon nitride heterojunctions as well as very recent developments in terms of multiphase heterojunction formation, including the latest insights into the anatase-rutile system. When carefully designed, semiconductor composites comprising two or three different materials or phases very effectively facilitate charge separation and charge carrier transfer, substantially improving photocatalytic and photoelectrochemical effi ciency.

The carbon electrode in nonaqueous Li-O2 cells.

Abstract: Carbon has been used widely as the basis of porous cathodes for nonaqueous Li–O2 cells. However, the stability of carbon and the effect of carbon on electrolyte decomposition in such cells are complex and depend on the hydrophobicity/hydrophilicity of the carbon surface. Analyzing carbon cathodes, cycled in Li–O2 cells between 2 and 4 V, using acid treatment and Fenton’s reagent, and combined with differential electrochemical mass spectrometry and FTIR, demonstrates the following: Carbon is relatively stable below 3.5 V (vs Li/Li+) on discharge or charge, especially so for hydrophobic carbon, but is unstable on charging above 3.5 V (in the presence of Li2O2), oxidatively decomposing to form Li2CO3. Direct chemical reaction with Li2O2 accounts for only a small proportion of the total carbon decomposition on cycling. Carbon promotes electrolyte decomposition during discharge and charge in a Li–O2 cell, giving rise to Li2CO3 and Li carboxylates (DMSO and tetraglyme electrolytes). The Li2CO3 and Li carboxylat...