Extrapolate: generalizing counterexamples of functional test properties

This paper presents a new tool called Extrapolate that automatically generalizes counterexamples found by property-based testing in Haskell. Example applications show that generalized counterexamples can inform the programmer more fully and more immediately what characterises failures. Extrapolate is able to produce more general results than similar tools. Although it is intrinsically unsound, as reported generalizations are based on testing, it works well for examples drawn from previous published work in this area

Symbolic AI for XAI: Evaluating LFIT inductive programming for explaining biases in machine learning

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicityThis work was supported by projects: PRIMA (H2020-MSCA-ITN-2019-860315), TRESPASS-ETN(H2020-MSCA-ITN-2019-860813), IDEA-FAST (IMI2-2018-15-853981), BIBECA(RTI2018-101248-B-I00MINECO/FEDER), RTI2018-095232-B-C22MINECO, PLeNTaS project PID2019-111430RBI00MINECO; and also by Pays de la Loire Region through RFI Atlanstic 202

Genericidad de funciones: el <i>quid</i> para la incorporación de dominios en un Sistema Funcional Inductivo basado en Haskell

Hoy en día, una inconmensurable cantidad de programas informáticos se encuentran en ejecución generando información propia de su comportamiento, estos tipos de historial son amplia y generalmente conocidos como Logs. Sin embargo, a pesar de los avances en la inferencia funcional inductiva para trabajar con los datos, hasta el momento se ha prestado escasa atención a la automatización del procesamiento analítico de estos tipos de registros de eventos. En este sentido, puesto que la alta expresividad de los Lenguajes de Programación Declarativos es una noción ampliamente aceptada, en este trabajo se aborda las implicancias prácticas de la Programación Funcional Inductiva aplicado en el dominio específico de los Logs para su inferencia asistida ex professo.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Genericidad de funciones: el <i>quid</i> para la incorporación de dominios en un Sistema Funcional Inductivo basado en Haskell

Hoy en día, una inconmensurable cantidad de programas informáticos se encuentran en ejecución generando información propia de su comportamiento, estos tipos de historial son amplia y generalmente conocidos como Logs. Sin embargo, a pesar de los avances en la inferencia funcional inductiva para trabajar con los datos, hasta el momento se ha prestado escasa atención a la automatización del procesamiento analítico de estos tipos de registros de eventos. En este sentido, puesto que la alta expresividad de los Lenguajes de Programación Declarativos es una noción ampliamente aceptada, en este trabajo se aborda las implicancias prácticas de la Programación Funcional Inductiva aplicado en el dominio específico de los Logs para su inferencia asistida ex professo.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Genericidad de funciones: el <i>quid</i> para la incorporación de dominios en un Sistema Funcional Inductivo basado en Haskell

Hoy en día, una inconmensurable cantidad de programas informáticos se encuentran en ejecución generando información propia de su comportamiento, estos tipos de historial son amplia y generalmente conocidos como Logs. Sin embargo, a pesar de los avances en la inferencia funcional inductiva para trabajar con los datos, hasta el momento se ha prestado escasa atención a la automatización del procesamiento analítico de estos tipos de registros de eventos. En este sentido, puesto que la alta expresividad de los Lenguajes de Programación Declarativos es una noción ampliamente aceptada, en este trabajo se aborda las implicancias prácticas de la Programación Funcional Inductiva aplicado en el dominio específico de los Logs para su inferencia asistida ex professo.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Symbolic XAI: automatic programming II

Explainable artificial intelligence (XAI) is a field blooming right now. With the popularity of opaque systems, the need of explanation methods that shed light on how this systems works has risen as well. In this work, we propose the usage of symbolic machine learning systems as explanation methods, a line that is yet to be fully explored. We will do this by reviewing this symbolic systems, analyzing the existing taxonomies of explanation methods and fitting the systems within the taxonomies. Finally, we will also do some testing on solving numerical problems with symbolic systems

Learning from interpreting transitions in explainable deep learning for biometrics

Máster Universitario en Métodos Formales en Ingeniería InformáticaWith the rapid development of machine learning algorithms, it has been applied to almost every aspect of tasks, such as natural language processing, marketing prediction. The usage of machine learning algorithms is also growing in human resources departments like the hiring pipeline. However, typical machine learning algorithms learn from the data collected from society, and therefore the model learned may inherently reflect the current and historical biases, and there are relevant machine learning algorithms that have been shown to make decisions largely influenced by gender or ethnicity. How to reason about the bias of decisions made by machine learning algorithms has attracted more and more attention. Neural structures, such as deep learning ones (the most successful machine learning based on statistical learning) lack the ability of explaining their decisions. The domain depicted in this point is just one example in which explanations are needed. Situations like this are in the origin of explainable AI. It is the domain of interest for this project. The nature of explanations is rather declarative instead of numerical. The hypothesis of this project is that declarative approaches to machine learning could be crucial in explainable A