Publications

This article is a legal and technical study of the intersection between Trustworthy AI and Labour Law. We propose a tripartite taxonomy to understand the implications of the AI Act in the field of Labour Law.

Deep Ensembles can improve performance but may also introduce fairness issues, unevenly benefiting different groups. This study identifies that effect, propose a explanation based on predictive diversity and explores mitigation techniques that can reduce unfairness while preserving performance gains.

We define three metrics of the group information power (social capital) in a network based on effective resistance (spectral graph theory). We propose also three metrics of social capital unfairness (structural group unfairness) and a heuristic to mitigate it.

FairShap, i.e. Fair Shapley Values, is a family of data valuation functions for Algorithmic Fairness based on Game Theory which can be used as a novel, interpretable, pre-processing and model-agnostic (re-weighting) method for fair algorithmic decision-making.

Theoretical and empirical framework to analyze and perform graph rewiring in a principled way. Also, proposal of calulation of Commute Times (resistance) in a GNN layer and Bottleneck minimizarion using Spectral gradients.

A big step done in graph statistical analysis to unveil the informal structure of the knowledge area of business organization in Spain using TESEO database.

Describing the technical and basic security features of the most popular telemedicine videoconferencing software.

Understanding and measuring the scientific structure of the computer Science field in Spain.

Deepening in cognitive behaviour with eye-tracking techniques and Machine Learning analysis.

Graph statistical analysis to unveil the informal structure of the knowledge area of business organization in Spain using TESEO database.

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