Paving the Way to Explainable Artificial Intelligence with Fuzzy Modeling

Mencar, Corrado; Alonso, José M.

doi:10.1007/978-3-030-12544-8_17

Cited by 30 publications

(19 citation statements)

References 41 publications

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“… Hybrid models encompassing linguistic rules and data-based learning techniques, capable of supporting the transition from the traditional way of doing to the new data-based modeling era in the management of ITS systems. We foresee that the community will witness a renaissance of data mining methods incorporating methods such as fuzzy logic not only to implement human knowledge to decision workflows, but also to explain and describe the internal structure of learned models, as it is currently under investigation in many contributions under the XAI umbrella [ 212 , 213 ]. New prescriptive data-based techniques such as Deep Reinforcement Learning [ 214 ] and Algorithmic Game Theory [ 215 ] will also grasp interest in the near future for their close connection to actionable data science.…”

Section: Emerging Ai Areas Towards Actionable Itsmentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

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Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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Section: Emerging Ai Areas Towards Actionable Itsmentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

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“…However, that study suggests that those contributions can be linked to address the challenges arising in that field. In this regard, while potential options to develop XAI systems with fuzzy modeling have been proposed by Mencar and Alonso [31], non-fuzzy options have been proposed by Adadi and Berrada [32].…”

Section: Related Workmentioning

confidence: 99%

Contextualizing Support Vector Machine Predictions

Loor¹,

Tré²

2020

IJCIS

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Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to. Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use of augmented appraisal degrees to put those predictions in context. A use case, in which the classes of handwritten digits are predicted, illustrates how the interpretability of such predictions is benefitted from their contextualization.

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“…The results of that study suggest that, although those efforts seem to be distant with the efforts made by the non-fuzzy community, both efforts can be linked to address the challenges arising in that field. In this regard, while potential options in the fuzzy logic community can be found in [14], non-fuzzy options oriented to explain individual predictions can be found in [1].…”

Section: Related Workmentioning

confidence: 99%

Explaining Computer Predictions with Augmented Appraisal Degrees

Loor

Tré

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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An augmented appraisal degree (AAD) has been conceived as a mathematical representation of the connotative meaning in an experience-based evaluation, which depends on a particular experience or knowledge. Aiming to improve the interpretability of computer predictions, we explore the use of AADs to represent evaluations that are performed by a machine to predict the class of a particular object. Hence, we propose a novel method whereby predictions made using a support vector machine classification process are augmented through AADs. An illustrative example, in which the classes of handwritten digits are predicted, shows how the augmentation of such predictions can favor their interpretability.

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Paving the Way to Explainable Artificial Intelligence with Fuzzy Modeling

Cited by 30 publications

References 41 publications

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Contextualizing Support Vector Machine Predictions

Explaining Computer Predictions with Augmented Appraisal Degrees

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