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Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma

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Measuring Bias in AI Models: An Statistical Approach Introdu...

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Daniel DeAlcala Ignacio Serna Aythami Morales Julian Fierrez Javier Ortega-Garcia Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests.

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DeepWriteSYN: On-line handwriting synthesis via deep short-term representations

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arXiv 2020年

作者： Tolosana, Ruben Delgado-Santos, Paula Perez-Uribe, Andres Vera-Rodriguez, Ruben Fierrez, Julian Morales, Aythami Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Switzerland

This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. It comprises two modules: i) an optional and interchangeable temporal segmentation, which divides the handwriting into short-time segments consisting of individual or multiple concatenated strokes;and ii) the on-line synthesis of those short-time handwriting segments, which is based on a sequence-to-sequence Variational Autoencoder (VAE). The main advantages of the proposed approach are that the synthesis is carried out in short-time segments (that can run from a character fraction to full characters) and that the VAE can be trained on a configurable handwriting dataset. These two properties give a lot of flexibility to our synthesiser, e.g., as shown in our experiments, DeepWriteSYN can generate realistic handwriting variations of a given handwritten structure corresponding to the natural variation within a given population or a given subject. These two cases are developed experimentally for individual digits and handwriting signatures, respectively, achieving in both cases remarkable results. Also, we provide experimental results for the task of on-line signature verification showing the high potential of DeepWriteSYN to improve significantly one-shot learning scenarios. To the best of our knowledge, this is the first synthesis approach capable of generating realistic on-line handwriting in the short term (including handwritten signatures) via deep learning. This can be very useful as a module toward long-term realistic handwriting generation either completely synthetic or as natural variation of given handwriting samples. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Is my data in your AI Model? Membership Inference Test with Application to Face Images

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arXiv 2024年

作者： DeAlcala, Daniel Morales, Aythami Fierrez, Julian Mancera, Gonzalo Tolosana, Ruben Ortega-Garcia, Javier The Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Madrid28049 Spain

This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifically, we propose two MINT architectures designed to learn the distinct activation patterns that emerge when an Audited Model is exposed to data used during its training process. These architectures are based on Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The experimental framework focuses on the challenging task of Face Recognition, considering three state-of-the-art Face Recognition systems. Experiments are carried out using six publicly available databases, comprising over 22 million face images in total. Different experimental scenarios are considered depending on the context of the AI model to test. Our proposed MINT approach achieves promising results, with up to 90% accuracy, indicating the potential to recognize if an AI model has been trained with specific data. The proposed MINT approach can serve to enforce privacy and fairness in several AI applications, e.g., revealing if sensitive or private data was used for training or tuning Large Language Models (LLMs). © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

Combining Blockchain and biometrics: A Survey on Technical Aspects and a First Legal Analysis

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arXiv 2023年

作者： Ghafourian, Mahdi Sumer, Bilgesu Vera-Rodriguez, Ruben Fierrez, Julian Tolosana, Ruben Moralez, Aythami Kindt, Els Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain KU Leuven Belgium

Biometric recognition as a unique, hard-to-forge, and efficient way of identification and verification has become an indispensable part of the current digital world. The fast evolution of this technology has been a strong incentive for integrating it into many applications. Meanwhile, blockchain, the very attractive decentralized ledger technology, has been widely received both by the research and industry in the past years and it is being increasingly deployed nowadays in many different applications, such as money transfer, IoT, healthcare, or logistics. Recently, researchers have started to speculate what would be the pros and cons and what would be the best applications when these two technologies cross paths. This paper provides a survey of technical literature research on the combination of blockchain and biometrics and includes a first legal analysis of this integration to shed light on challenges and potentials. While this combination is still in its infancy and a growing body of literature discusses specific blockchain applications and solutions in an advanced technological set-up, this paper presents a holistic understanding of blockchain applicability in the biometric sector. This study demonstrates that combining blockchain and biometrics would be beneficial for novel applications in biometrics such as the PKI mechanism, distributed trusted service, and identity management. However, blockchain networks at their current stage are not efficient and economical for real-time applications. From a legal point of view, the allocation of accountability remains a main issue, while other difficulties remain, such as conducting a proper data Protection Impact Assessment. Finally, it supplies technical and legal recommendations to reap the benefits and mitigate the risks of the combination. © 2023, CC BY-NC-ND.

关键词： Blockchain

FaceGenderID: Exploiting Gender Information in DCNNs Face Recognition Systems

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FaceGenderID: Exploiting Gender Information in DCNNs Face Re...

IEEE Computer Society Conference on Computer Vision and pattern Recognition Workshops (CVPRW)

作者： Ruben Vera-Rodriguez Marta Blazquez Aythami Morales Ester Gonzalez-Sosa João C. Neves Hugo Proença Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Madrid Spain Nokia Bell Labs Madrid Spain University of Beira Interior Covilha Portugal

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

This paper addresses the effect of gender as a covariate in face verification systems. Even though pre-trained models based on Deep Convolutional Neural Networks (DCNNs), such as VGG-Face or ResNet-50, achieve very high performance, they are trained on very large datasets comprising millions of images, which have biases regarding demographic aspects like the gender and the ethnicity among others. In this work, we first analyse the separate performance of these state-of-the-art models for males and females. We observe a gap between face verification performances obtained by both gender classes. These results suggest that features obtained by biased models are affected by the gender covariate. We propose a gender-dependent training approach to improve the feature representation for both genders, and develop both: i) gender specific DCNNs models, and ii) a gender balanced DCNNs model. Our results show significant and consistent improvements in face verification performance for both genders, individually and in general with our proposed approach. Finally, we announce the availability (at GitHub) of the FaceGenderID DCNNs models proposed in this work, which can support further experiments on this topic.

关键词： Face Face recognition Training databases Estimation data models

ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection

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arXiv 2022年

作者： Ruiz-Garcia, Juan Carlos Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Herreros-Rodriguez, Jaime The Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain The Hospital Universitario Infanta Leonor Madrid28031 Spain

This article presents a comprehensive analysis of the different tests proposed in the recent ChildCI framework, proving its potential for generating a better understanding of children's neuromotor and cognitive development along time, as well as their possible application in other research areas such as e-Health and e-Learning. In particular, we propose a set of over 100 global features related to motor and cognitive aspects of the children interaction with mobile devices, some of them collected and adapted from the literature. Furthermore, we analyse the robustness and discriminative power of the proposed feature set including experimental results for the task of children age group detection based on their motor and cognitive behaviours. Two different scenarios are considered in this study: i) single-test scenario, and ii) multiple-test scenario. Results over 93% accuracy are achieved using the publicly available ChildCIdb_v1 database (over 400 children from 18 months to 8 years old), proving the high correlation of children's age with the way they interact with mobile devices. © 2022, CC BY-NC-ND.

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Longitudinal Analysis and Quantitative Assessment of Child Development through Mobile Interaction

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arXiv 2024年

作者： Ruiz-Garcia, Juan Carlos Tolosana, Ruben Vera-Rodriguez, Ruben Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Herreros-Rodriguez, Jaime The Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain The Hospital Universitario Infanta Leonor Madrid28031 Spain

This article provides a comprehensive overview of recent research in the area of Child-Computer Interaction (CCI). The main contributions of the present article are two-fold. First, we present a novel longitudinal CCI database named ChildCIdbLong1, which comprises over 600 children aged 18 months to 8 years old, acquired continuously over 4 academic years (2019-2023). As a result, ChildCIdbLong comprises over 12K test acquisitions over a tablet device. Different tests are considered in ChildCIdbLong, requiring different touch and stylus gestures, enabling the evaluation of praxical and cognitive skills such as attentional, visuo-spatial, and executive, among others. In addition to the ChildCIdbLong database, we propose a novel quantitative metric called Test Quality (Q), designed to measure the motor and cognitive development of children through their interaction with a tablet device. In order to provide a better comprehension of the proposed Q metric, popular percentile-based growth representations are introduced for each test, providing a two-dimensional space to compare children's development with respect to the typical age skills of the population. The results achieved in the present article highlight the potential of the novel ChildCIdbLong database in conjunction with the proposed Q metric to measure the motor and cognitive development of children as they grow up. The proposed framework could be very useful as an automatic tool to support child experts (e.g., paediatricians, educators, or neurologists) for early detection of potential physical/cognitive impairments during children's development. © 2024, CC BY-NC-ND.

关键词： Electronic health record

Children Age Group Detection based on Human-Computer Interaction and Time Series Analysis

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arXiv 2024年

作者： Ruiz-Garcia, Juan Carlos Hojas, Carlos Tolosana, Ruben Vera-Rodriguez, Ruben Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Herreros-Rodriguez, Jaime The Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain The Hospital Universitario Infanta Leonor Madrid28031 Spain

This article proposes a novel Children-Computer Interaction (CCI) approach for the task of age group detection. This approach focuses on the automatic analysis of the time series generated from the interaction of the children with mobile devices. In particular, we extract a set of 25 time series related to spatial, pressure, and kinematic information of the children interaction while colouring a tree through a pen stylus tablet, a specific test from the large-scale public ChildCIdb database1. A complete analysis of the proposed approach is carried out using different time series selection techniques to choose the most discriminative ones for the age group detection task: i) a statistical analysis, and ii) an automatic algorithm called Sequential Forward Search (SFS). In addition, different classification algorithms such as Dynamic Time Warping Barycenter Averaging (DBA) and Hidden Markov Models (HMM) are studied. Accuracy results over 85% are achieved, outperforming previous approaches in the literature and in more challenging age group conditions. Finally, the approach presented in this study can benefit many children-related applications, for example, towards an age-appropriate environment with the technology. © 2024, CC BY-NC-ND.

关键词： Time series analysis

GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection

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arXiv 2019年

作者： Neves, João C. Tolosana, Ruben Vera-Rodriguez, Ruben Lopes, Vasco Proença, Hugo Fierrez, Julian NOVA-LINCS Portugal The Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Spain University of Beira Interior Portugal IT - Instituto de Telecomunicações Portugal

The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. In this study, we focus on the synthesis of entire facial images, which is a specific type of facial manipulation. The main contributions of this study are four-fold: i) a novel strategy to remove GAN "fingerprints" from synthetic fake images based on autoencoders is described, in order to spoof facial manipulation detection systems while keeping the visual quality of the resulting images;ii) an in-depth analysis of the recent literature in facial manipulation detection;iii) a complete experimental assessment of this type of facial manipulation, considering the state-of-the-art fake detection systems (based on holistic deep networks, steganalysis, and local artifacts), remarking how challenging is this task in unconstrained scenarios;and finally iv) we announce a novel public database, named iFakeFaceDB, yielding from the application of our proposed GAN-fingerprint Removal approach (GANprintR) to already very realistic synthetic fake images. The results obtained in our empirical evaluation show that additional efforts are required to develop robust facial manipulation detection systems against unseen conditions and spoof techniques, such as the one proposed in this study. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition