Gordaliza Pastor, Paula

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https://academica-e.unavarra.es/handle/2454/50989

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Gordaliza Pastor

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Paula

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Estadística, Informática y Matemáticas

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0000-0002-0455-1200

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1175401

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812701

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2022 - 20233
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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Preserving the fairness guarantees of classifiers in changing environments: a survey
    (Association for Computing Machinery, 2023) Barrainkua, Ainhize; Gordaliza Pastor, Paula; Lozano, José Antonio; Quadrianto, Novi; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    The impact of automated decision-making systems on human lives is growing, emphasizing the need for these systems to be not only accurate but also fair. The ield of algorithmic fairness has expanded signiicantly in the past decade, with most approaches assuming that training and testing data are drawn independently and identically from the same distribution. However, in practice, diferences between the training and deployment environments exist, compromising both the performance and fairness of the decision-making algorithms in real-world scenarios. A new area of research has emerged to address how to maintain fairness guarantees in classiication tasks when the data generation processes difer between the source (training) and target (testing) domains. The objective of this survey is to ofer a comprehensive examination of fair classiication under distribution shift by presenting a taxonomy of current approaches. The latter is formulated based on the available information from the target domain, distinguishing between adaptive methods, which adapt to the target environment based on available information, and robust methods, which make minimal assumptions about the target environment. Additionally, this study emphasizes alternative benchmarking methods, investigates the interconnection with related research ields, and identiies potential avenues for future research.
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    PublicationOpen Access
    Making data fair through optimal trimmed matching
    (Springer, 2022-08-25) Inouzhe, Hristo; Gordaliza Pastor, Paula; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    Algorithmic fairness is one of the main concerns of today's scientific society due to the generalization of predictive algorithms in all aspects of human life. The aim of this work is to check if there is group bias in the response variable Y with respect to a sensitive information S present in the data. However, not all individuals in S are comparable, and some differences in the target Y may arise from genuine differences in the data. We propose to eliminate such cases by trimming an proportion of the input data as a pre-processing step to any further learning mechanism in order to obtain the two closest possible marginal distributions (with respect to S). On this population that is ¿similar enough¿ we can check for discrimination, in the sense of Demographic Parity. We solve a trimmed matching problem subject to fairness constraints that is a linear program that can be addressed with well-known techniques. We present some successful results of application to synthetic and real data.
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    PublicationOpen Access
    Interaction with a hand rehabilitation exoskeleton in EMG-driven bilateral therapy: influence of visual biofeedback on the users' performance
    (MDPI, 2023) Cisnal, Ana; Gordaliza Pastor, Paula; Pérez Turiel, Javier; Fraile, Juan Carlos; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    The effectiveness of EMG biofeedback with neurorehabilitation robotic platforms has not been previously addressed. The present work evaluates the influence of an EMG-based visual biofeedback on the user performance when performing EMG-driven bilateral exercises with a robotic hand exoskeleton. Eighteen healthy subjects were asked to perform 1-min randomly generated sequences of hand gestures (rest, open and close) in four different conditions resulting from the combination of using or not (1) EMG-based visual biofeedback and (2) kinesthetic feedback from the exoskeleton movement. The user performance in each test was measured by computing similarity between the target gestures and the recognized user gestures using the L2 distance. Statistically significant differences in the subject performance were found in the type of provided feedback (p-value 0.0124). Pairwise comparisons showed that the L2 distance was statistically significantly lower when only EMG-based visual feedback was present (2.89 ± 0.71) than with the presence of the kinesthetic feedback alone (3.43 ± 0.75, p-value = 0.0412) or the combination of both (3.39 ± 0.70, p-value = 0.0497). Hence, EMG-based visual feedback enables subjects to increase their control over the movement of the robotic platform by assessing their muscle activation in real time. This type of feedback could benefit patients in learning more quickly how to activate robot functions, increasing their motivation towards rehabilitation.