Ettore

Early Stage Researcher at CiTIUS, USC

Ettore has been working on artificial intelligence models being applied in different domains, now his focus is about extracting the learnt knowledge of black-box AI in a human comprehensible way. He enjoys cooking and singing.
USC - University of Santiago de Compostela (Spain) CiTIUS - Research Centre in Intelligent Technologies (Coordinator)

Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS)
Universidade de Santiago de Compostela (USC)
Rúa de Jenaro de la Fuente Domínguez,
15782 – Santiago de Compostela, Spain.

Explaining black-box models in terms of grey-box twin models

PhD research topic

Objectives:

To define, design and develop a new methodology to explain black-box models in Natural Language. Taking as inspiration the paradigm of digital twins that is applied to industry 4.0, the ESR will generate explainable models as digital twins of black-box models. The target will be explaining neural networks, including deep architectures. The explainable twin model will be supported by a pool of grey-box models such as Bayesian networks, interpretable fuzzy systems and decision trees; endowed with a linguistic layer for providing expert and non-expert users with explanations in Natural Language.

use cases:

– explaining the outcomes of models for automatic description of human faces, for example in security applications (defined in a secondment at Università ta’ Malta); and

– a real use case (“Experiences, offering, demand and classification in semantic-search processes applied to business processes”) defined in an inter-sectoral secondment (Indra, Spain).

Expected Results:

(1) Theoretical understanding of the state of the art in relevant aspects of machine learning methods (paying special attention to Neural Networks / Deep Learning approaches) and the paradigm of digital twins.

(2) SWOT analysis of existing methods for explaining black-box models and study of methods for explaining grey-box models in Natural Language.

(3) Definition and implementation of a new methodology for explaining black-box models in Natural Language.

(4) Validation of this methodology in two use cases where the goodness of the generated explanations will be evaluated by humans.

(5) A fully functional web service coming from (3) and (4).

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