Explainable Artificial Intelligence in Data Science: From Foundational Issues Towards Socio-technical Considerations

A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already afects many sensitive realms (as in Politics, Social Sciences, or Law). The decision making power handover to opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solu tions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientifc and humanistic areas. The frst aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is benefciated from part of the scientifc and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the feld of Data Science. The second objective is to develop some arguments justifying the authors’ bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justifcation and the explainer-explainee agreement.Agencia Estatal de Investigación PID2019-109152GB-I00/AEI/10.13039/50110001103

On the Soundness of XAI in Prognostics and Health Management (PHM)

The aim of Predictive Maintenance, within the field of Prognostics and Health Management (PHM), is to identify and anticipate potential issues in the equipment before these become critical. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as Remaining Useful Life (RUL). Deep Learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that this kind of black box models are opaque decision systems, and it may be hard to explain its outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This work presents a critical and comparative revision on a number of XAI methods applied on time series regression model for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. The model used during the experimentation is a DCNN trained to predict the RUL of an aircraft engine. The methods are reviewed and compared using a set of metrics that quantifies a number of desirable properties that any XAI method should fulfill. The results show that GRAD-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of Image Processing

Extracción y organización del conocimiento de etiquetados. Aplicación a etiquetados en repositorios digitales sobre arte

Junta de Andalucía TIC-606

Modeling Lexicon Emergence as Concept Emergence in Networks

Amodel for lexicon emergence in social networks is presented. The model is based on a modified version of classic Naming Games, where agents’ knowledge is represented by means of formal contexts. That way it is possible to represent the effect interactions have on individual knowledge as well as the dynamics of global knowledge in the network.Ministerio de Economía y Competitividad TIN2013-41086-PJunta de Andalucía TIC-606

Building knowledge layers and networks from urban digital information

The understanding and management of complex digital information on cities need the use of tools providing experts with new insights about the knowledge hidden within this great amount of data. In this paper a methodology to provide such a kind of knowledge is presented. This methodology is based on Formal Concept Analysis and allows visualizing abstract concepts that can be interpreted (and hence discovered) by city researchers.Peer Reviewe

Complex concept lattices for simulating human prediction in sport

In order to address the study of complex systems, the detection of patterns in their dynamics could play a key role in understanding their evolution. In particular, global patterns are required to detect emergent concepts and trends, some of them of a qualitative nature. Formal concept analysis (FCA) is a theory whose goal is to discover and extract knowledge from qualitative data (organized in concept lattices). In complex environments, such as sport competitions, the large amount of information currently available turns concept lattices into complex networks. The authors analyze how to apply FCA reasoning in order to increase confidence in sports predictions by means of detecting regularities from data through the management of intuitive and natural attributes extracted from publicly available information. The complexity of concept lattices -considered as networks with complex topological structure- is analyzed. It is applied to building a knowledge based system for confidence-based reasoning, which simulates how humans tend to avoid the complexity of concept networks by means of bounded reasoning skills.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606

Qualitative Reasoning on Complex Systems from Observations

A hybrid approach to phenomenological reconstruction of Complex Systems (CS), using Formal Concept Analysis (FCA) as main tool for conceptual data mining, is proposed. To illustrate the method, a classic CS is selected (cellular automata), to show how FCA can assist to predict CS evolution under different conceptual descriptions (from different observable features of the CS).Junta de Andalucía TIC-606

Extracting emergent knowledge about the socioeconomic urban contexts

An approach to represent and analyze socioeconomic contexts as well as to reason with them, in order to extract useful conclusions about the social perception emerging from citizens’ beliefs and feelings, is introduced. We concentrate here in the formal aspects of the solution, completing this way our workMinisterio de Economía y Competitividad TIN2013-41086-PJunta de Andalucía TIC-606

Simulating Language Dynamics by Means of Concept Reasoning

A problem in the phenomenological reconstruction of Complex Systems (CS) is the extraction of the knowledge that elements playing in CS use during its evolution. This problem is important because such a knowledge would allow the researcher to understand the global behavior of the system [1,2]. In this paper an approach to partially solve this problem by means of Formal Concept Analysis (FCA) is described in a particular case, namely Language Dynamics. The main idea lies in the fact that global knowledge in CS is naturally built by local interactions among agents, and FCA could be useful to represent their own knowledge. In this way it is possible to represent the effect of interactions on individual knowledge as well as the dynamics of global knowledge. Experiments in order to show this approach are given using WordNet.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606

Confidence-Based Reasoning with Local Temporal Formal Contexts

Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge based system for confidence reasoning.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606