Articles producció científica> Enginyeria Informàtica i Matemàtiques

Generating Deep Learning Model-Specific Explanations at the End User's Side

  • Datos identificativos

    Identificador: imarina:9291527
    Handle: https://hdl.handle.net/20.500.11797/imarina9291527
    Autores:
    Haffar, RJebreel, NSanchez, DDomingo-Ferrer, J
    Resumen:
    End users who cannot afford to collect and label big data to train accurate deep learning (DL) models resort to Machine Learning as a Service (MLaaS) providers, who provide paid access to accurate DL models. However, the lack of transparency in how the providers' models make predictions causes a problem of trust. A way to increase trust (and also to align with ethical regulations) is for predictions to be accompanied by explanations locally and independently generated by the end users (rather than by explanations offered by the model providers). Explanation methods using internal components of DL models (a.k.a. model-specific explanations) are more accurate and effective than those relying solely on the inputs and outputs (a.k.a. model-agnostic explanations). However, end users lack white-box access to the internal components of the providers' models. To tackle this issue, we propose a novel approach allowing an end user to locally generate model-specific explanations for a DL classification model accessed via a provider's API. First, we approximate the provider's model with a local surrogate model. We then use the surrogate model's components to locally generate model-specific explanations that approximate the explanations obtainable with white-box access to the provider's DL model. Specifically, we leverage the surrogate model's gradients to generate adversarial examples that counterfactually explain why an input example is classified into a specific class. Our approach only requires the end user to have unlabeled data of size 0.5% of the provider's training data and with a similar distribution; given the small size and unlabeled nature of these data, they can be assumed to be already available to the end user or even to be supplied by the provider to build trust in h

  • Otros:

    Autor según el artículo: Haffar, R; Jebreel, N; Sanchez, D; Domingo-Ferrer, J
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Domingo Ferrer, Josep / Haffar, Rami / Jebreel, Najeeb Moharram Salim / Sánchez Ruenes, David
    Palabras clave: Recognition End-user explanations Deep learning classification models Counterfactual explanations Adversarial examples deep learning classification models counterfactual explanations adversarial examples
    Resumen: End users who cannot afford to collect and label big data to train accurate deep learning (DL) models resort to Machine Learning as a Service (MLaaS) providers, who provide paid access to accurate DL models. However, the lack of transparency in how the providers' models make predictions causes a problem of trust. A way to increase trust (and also to align with ethical regulations) is for predictions to be accompanied by explanations locally and independently generated by the end users (rather than by explanations offered by the model providers). Explanation methods using internal components of DL models (a.k.a. model-specific explanations) are more accurate and effective than those relying solely on the inputs and outputs (a.k.a. model-agnostic explanations). However, end users lack white-box access to the internal components of the providers' models. To tackle this issue, we propose a novel approach allowing an end user to locally generate model-specific explanations for a DL classification model accessed via a provider's API. First, we approximate the provider's model with a local surrogate model. We then use the surrogate model's components to locally generate model-specific explanations that approximate the explanations obtainable with white-box access to the provider's DL model. Specifically, we leverage the surrogate model's gradients to generate adversarial examples that counterfactually explain why an input example is classified into a specific class. Our approach only requires the end user to have unlabeled data of size 0.5% of the provider's training data and with a similar distribution; given the small size and unlabeled nature of these data, they can be assumed to be already available to the end user or even to be supplied by the provider to build trust in his model. We demonstrate the accuracy and effectiveness of our approach through extensive experiments on two ML tasks: image classification and tabular data classification. The locally generated explanations are consistent with those obtainable with white-box access to the provider's model, thus giving end users an independent and reliable way to determine if the provider's model is trustworthy.
    Áreas temáticas: Software Matemática / probabilidade e estatística Interdisciplinar Information systems Engenharias iv Economia Control and systems engineering Computer science, artificial intelligence Ciência da computação Artificial intelligence
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: rami.haffar@urv.cat najeeb.jebreel@urv.cat rami.haffar@urv.cat najeeb.jebreel@urv.cat david.sanchez@urv.cat josep.domingo@urv.cat
    Identificador del autor: 0000-0001-7275-7887 0000-0001-7213-4962
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://www.worldscientific.com/doi/abs/10.1142/S0218488522400219
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: International Journal Of Uncertainty Fuzziness And Knowledge-Based Systems. 30 (SUPP02): 255-278
    Referencia de l'ítem segons les normes APA: Haffar, R; Jebreel, N; Sanchez, D; Domingo-Ferrer, J (2022). Generating Deep Learning Model-Specific Explanations at the End User's Side. International Journal Of Uncertainty Fuzziness And Knowledge-Based Systems, 30(SUPP02), 255-278. DOI: 10.1142/S0218488522400219
    DOI del artículo: 10.1142/S0218488522400219
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications

  • Palabras clave:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Control and Systems Engineering,Information Systems,Software
    Recognition
    End-user explanations
    Deep learning classification models
    Counterfactual explanations
    Adversarial examples
    deep learning classification models
    counterfactual explanations
    adversarial examples
    Software
    Matemática / probabilidade e estatística
    Interdisciplinar
    Information systems
    Engenharias iv
    Economia
    Control and systems engineering
    Computer science, artificial intelligence
    Ciência da computação
    Artificial intelligence

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