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

Predicting Community Evolution in Social Networks

Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page

A modular modelling framework for hypotheses testing in the simulation of urbanisation

In this paper, we present a modelling experiment developed to study systems of cities and processes of urbanisation in large territories over long time spans. Building on geographical theories of urban evolution, we rely on agent-based models to 1/ formalise complementary and alternative hypotheses of urbanisation and 2/ explore their ability to simulate observed patterns in a virtual laboratory. The paper is therefore divided into two sections : an overview of the mechanisms implemented to represent competing hypotheses used to simulate urban evolution; and an evaluation of the resulting model structures in their ability to simulate - efficiently and parsimoniously - a system of cities (the Former Soviet Union) over several periods of time (before and after the crash of the USSR). We do so using a modular framework of model-building and evolutionary algorithms for the calibration of several model structures. This project aims at tackling equifinality in systems dynamics by confronting different mechanisms with similar evaluation criteria. It enables the identification of the best-performing models with respect to the chosen criteria by scanning automatically the parameter along with the space of model structures (as combinations of modelled dynamics).Comment: 21 pages, 3 figures, working pape

Advancing systems biology of yeast through machine learning and comparative genomics

Synthetic biology has played a pivotal role in accomplishing the production of high value commodities, pharmaceuticals, and bulk chemicals. Fueled by the breakthrough of synthetic biology and metabolic engineering, Saccharomyces cerevisiae and various other yeasts (such as Yarrowia lipolytica, Pichia pastoris) have been proven to be promising microbial cell factories and are frequently used in scientific studies. However, the cellular metabolism and physiological properties for most of the yeast species have not been characterized in detail. To address these knowledge gaps, this thesis aims to leverage the large amounts of data available for yeast species and use state-of-the-art machine learning techniques and comparative genomic analysis to gain a deeper insight into yeast traits and metabolism.In this thesis, machine learning was applied to various unresolved biological problems on yeasts, i.e., gene essentiality, enzyme turnover number (kcat), and protein production. In the first part of the work, machine learning approaches were employed to predict gene essentiality based on sequence features and evolutionary features. It was demonstrated that the essential gene prediction could be substantially improved by integrating evolution-based features. Secondly, a high-quality deep learning model DLKcat was developed to predict kcat\ua0values by combining a graph neural network for substrates and a convolutional neural network for proteins. By predicting kcat profiles for 343 yeast/fungi species, enzyme-constrained models were reconstructed and used to further elucidate the cellular metabolism on a large scale. Lastly, a random forest algorithm was adopted to investigate feature importance analysis on protein production, it was found that post-translational modifications (PTMs) have a relatively higher impact on protein production compared with amino acid composition. In comparative genomics, a comprehensive toolbox HGTphyloDetect was developed to facilitate the identification of horizontal gene transfer (HGT) events. Case studies on some yeast species demonstrated the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, through systematic evolution analysis (e.g., HGT, gene family expansion) and genome-scale metabolic model simulation, the underlying mechanisms for substrate utilization were further probed across large-scale yeast species

Position specific variation in the rate of evolution in transcription factor binding sites

BACKGROUND: The binding sites of sequence specific transcription factors are an important and relatively well-understood class of functional non-coding DNAs. Although a wide variety of experimental and computational methods have been developed to characterize transcription factor binding sites, they remain difficult to identify. Comparison of non-coding DNA from related species has shown considerable promise in identifying these functional non-coding sequences, even though relatively little is known about their evolution. RESULTS: Here we analyse the genome sequences of the budding yeasts Saccharomyces cerevisiae, S. bayanus, S. paradoxus and S. mikatae to study the evolution of transcription factor binding sites. As expected, we find that both experimentally characterized and computationally predicted binding sites evolve slower than surrounding sequence, consistent with the hypothesis that they are under purifying selection. We also observe position-specific variation in the rate of evolution within binding sites. We find that the position-specific rate of evolution is positively correlated with degeneracy among binding sites within S. cerevisiae. We test theoretical predictions for the rate of evolution at positions where the base frequencies deviate from background due to purifying selection and find reasonable agreement with the observed rates of evolution. Finally, we show how the evolutionary characteristics of real binding motifs can be used to distinguish them from artefacts of computational motif finding algorithms. CONCLUSION: As has been observed for protein sequences, the rate of evolution in transcription factor binding sites varies with position, suggesting that some regions are under stronger functional constraint than others. This variation likely reflects the varying importance of different positions in the formation of the protein-DNA complex. The characterization of the pattern of evolution in known binding sites will likely contribute to the effective use of comparative sequence data in the identification of transcription factor binding sites and is an important step toward understanding the evolution of functional non-coding DNA

Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment