35 result(s)
Page Size: 10, 20, 50
Export: bibtex, xml, json, csv
Order by:

CNR Author operator: and / or
more
Typology operator: and / or
Language operator: and / or
Date operator: and / or
more
Rights operator: and / or
2020 Journal article Open Access OPEN

Causal inference for social discrimination reasoning
Qureshi B., Kamiran F., Karim A., Ruggieri S., Pedreschi D.
The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.Source: Journal of intelligent information systems 54 (2020): 425–437. doi:10.1007/s10844-019-00580-x
DOI: 10.1007/s10844-019-00580-x

See at: Journal of Intelligent Information Systems Open Access | ISTI Repository Open Access | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | link.springer.com Restricted | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | CNR ExploRA Restricted


2019 Journal article Open Access OPEN

A survey of methods for explaining black box models
Guidotti R., Monreale A., Ruggieri S., Turini F., Giannotti F., Pedreschi D.
In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.Source: ACM computing surveys 51 (2019). doi:10.1145/3236009
DOI: 10.1145/3236009
Project(s): SoBigData via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | dl.acm.org Open Access | ACM Computing Surveys Open Access | Archivio della Ricerca - Università di Pisa Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted | ACM Computing Surveys Restricted


2019 Conference article Open Access OPEN

On the stability of interpretable models
Guidotti R., Ruggieri S.
Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model's construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.Source: IJCNN 2019 - International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14-19 July, 2019
DOI: 10.1109/ijcnn.2019.8852158
Project(s): SoBigData via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | arxiv.org Open Access | ISTI Repository Open Access | academic.microsoft.com Restricted | arxiv.org Restricted | dblp.uni-trier.de Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | ui.adsabs.harvard.edu Restricted | www.arxiv-vanity.com Restricted | xplorestaging.ieee.org Restricted


2018 Contribution to book Open Access OPEN

How data mining and machine learning evolved from relational data base to data science
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M.
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17

See at: Archivio della Ricerca - Università di Pisa Open Access | ISTI Repository Open Access | academic.microsoft.com Restricted | arpi.unipi.it Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted


2018 Report Open Access OPEN

Assessing the stability of interpretable models
Guidotti R., Ruggieri S.
Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.Source: ISTI Technical reports, 2018
Project(s): SoBigData via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2018 Report Open Access OPEN

Local rule-based explanations of black box decision systems
Guidotti R., Monreale A., Ruggieri S., Pedreschi D., Turini F., Giannotti F.
The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts.% Therefore, we need explanations that reveals the reasons why a predictor takes a certain decision. In this paper we focus on the problem of black box outcome explanation, ie, explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box.Source: ISTI Technical reports, 2018
Project(s): SoBigData via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2018 Report Open Access OPEN

Open the black box data-driven explanation of black box decision systems
Pedreschi D., Giannotti F., Guidotti R., Monreale A., Pappalardo L., Ruggieri S., Turini F.
Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases hidden in the algorithms, due to human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions:(i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation;(ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance;(iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.Source: ISTI Technical reports, 2018
Project(s): SoBigData via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2017 Conference article Open Access OPEN

Efficiently clustering very large attributed graphs
Baroni A., Conte A., Patrignani M., Ruggieri S.
Attributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.Source: 2017 IEEE/ACM: International Conference on Advances in Social Networks Analysis and Mining 2017, July-August 2017
DOI: 10.1145/3110025.3110030

See at: arXiv.org e-Print Archive Open Access | arxiv.org Open Access | ISTI Repository Open Access | academic.microsoft.com Restricted | arxiv.org Restricted | arxiv.org Restricted | dl.acm.org Restricted | dl.acm.org Restricted | export.arxiv.org Restricted | CNR ExploRA Restricted


2016 Journal article Open Access OPEN

Big data research in Italy: a perspective
Bergamaschi S., Carlini E., Ceci M., Furletti B., Giannotti F., Malerba D., Mezzanzanica M., Monreale A., Pasi G., Pedreschi D., Perego R., Ruggieri S.
The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.Source: Engineering (Beijing) 2 (2016): 163–170. doi:10.1016/J.ENG.2016.02.011
DOI: 10.1016/j.eng.2016.02.011

See at: Engineering Open Access | Engineering Open Access | Engineering Open Access | Engineering Open Access | Engineering Open Access | Engineering Open Access | Engineering Open Access | Engineering Open Access | Engineering Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Engineering Open Access | Engineering Open Access | Engineering Open Access


2014 Journal article Open Access OPEN

A multidisciplinary survey on discrimination analysis
Romei A., Ruggieri S.
The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.Source: Knowledge engineering review (Print) 29 (2014): 582–638. doi:10.1017/S0269888913000039
DOI: 10.1017/s0269888913000039

See at: The Knowledge Engineering Review Open Access | The Knowledge Engineering Review Restricted | The Knowledge Engineering Review Restricted | The Knowledge Engineering Review Restricted | The Knowledge Engineering Review Restricted | journals.cambridge.org Restricted | CNR ExploRA Restricted | The Knowledge Engineering Review Restricted | The Knowledge Engineering Review Restricted | The Knowledge Engineering Review Restricted


2014 Journal article Open Access OPEN

Decision tree building on multi-core using FastFlow
Aldinucci M., Ruggieri S., Torquati M.
The whole computer hardware industry embraced the multi-core. The extreme optimisation of sequential algorithms is then no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an in-depth study of the parallelisation of an implementation of the C4.5 algorithm for multi-core architectures. We characterise elapsed time lower bounds for the forms of parallelisations adopted and achieve close to optimal performance. Our implementation is based on the FastFlow parallel programming environment, and it requires minimal changes to the original sequential code. Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.Source: Concurrency and computation 26 (2014): 800–820. doi:10.1002/cpe.3063
DOI: 10.1002/cpe.3063
Project(s): PARAPHRASE via OpenAIRE

See at: Concurrency and Computation Practice and Experience Open Access | Archivio Istituzionale Open Access | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | onlinelibrary.wiley.com Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | CNR ExploRA Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted


2013 Contribution to book Restricted

Discrimination Data Analysis: A Multi-disciplinary Bibliography
Romei A., Ruggieri S.
Discrimination data analysis has been investigated for the last fifty years in a large body of social, legal, and economic studies. Recently, discrimination discovery and prevention has become a blooming research topic in the knowledge discovery community. This chapter provides a multi-disciplinary annotated bibliography of the literature on discrimination data analysis, with the intended objective to provide a common basis to researchers from a multi-disciplinary perspective. We cover legal, sociological, economic and computer science referencesSource: Discrimination and Privacy in the Information Society, edited by Custers, Bart and Calders, Toon and Schermer, Bart and Zarsky, Tal, pp. 109–135, 2013
DOI: 10.1007/978-3-642-30487-3_6

See at: academic.microsoft.com Restricted | arpi.unipi.it Restricted | core.ac.uk Restricted | dblp.uni-trier.de Restricted | doi.org Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted | www.di.unipi.it Restricted | www.springerlink.com Restricted


2013 Journal article Open Access OPEN

Discrimination discovery in scientific project evaluation: a case study
Romei A., Ruggieri S., Turini F.
Discovering contexts of unfair decisions in a dataset of historical decision records is a non-trivial problem. It requires the design of ad hoc methods and techniques of analysis, which have to comply with existing laws and with legal argumentations. While some data mining techniques have been adapted to the purpose, the state-of-the-art of research still needs both methodological refinements, the consolidation of a Knowledge Discovery in Databases (KDD) process, and, most of all, experimentation with real data. This paper contributes by presenting a case study on gender discrimination in a dataset of scientific research proposals, and by distilling from the case study a general discrimination discovery process. Gender bias in scientific research is a challenging problem, that has been tackled in the social sciences literature by means of statistical regression. However, this approach is limited to test an hypothesis of discrimination over the whole dataset under analysis. Our methodology couples data mining, for unveiling previously unknown contexts of possible discrimination, with statistical regression, for testing the significance of such contexts, thus obtaining the best of the two worlds. (C) 2013 Elsevier Ltd. All rights reserved.Source: Expert systems with applications 40 (2013): 6064–6079. doi:10.1016/j.eswa.2013.05.016
DOI: 10.1016/j.eswa.2013.05.016

See at: Expert Systems with Applications Open Access | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted | CNR ExploRA Restricted | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted | Expert Systems with Applications Restricted


2013 Conference article Open Access OPEN

Data anonimity meets non-discrimination
Ruggieri S.
We investigate the relation between t-closeness, a well-known model of data anonymization, and alpha-protection, a model of data discrimination. We show that t-closeness implies bd(t)-protection, for a bound function bd() depending on the discrimination measure at hand. This allows us to adapt an inference control method, the Mondrian multidimensional generalization technique, to the purpose of non-discrimination data protection. The parallel between the two analytical models raises intriguing issues on the interplay between data anonymization and nondiscrimination research in data mining.Source: ICDMW 2013 - IEEE 13th International Conference on Data Mining Workshops, pp. 875–882, Dallas, Texas, USA, 7-10 December 2013
DOI: 10.1109/icdmw.2013.56

See at: www.di.unipi.it Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | doi.org Restricted | ieeexplore.ieee.org Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | www-kdd.isti.cnr.it Restricted | xplorestaging.ieee.org Restricted


2013 Conference article Open Access OPEN

Learning from polyhedral sets
Ruggieri S.
Parameterized linear systems allow for modelling and reasoning over classes of polyhedra. Collections of squares, rectangles, polytopes, and so on, can readily be defined by means of linear systems with parameters. In this paper, we investigate the problem of learning a parameterized linear system whose class of polyhedra includes a given set of example polyhedral sets and it is minimal.Source: IJCAI 2013 - Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1069–1075, Beijing, China, 3-9 August 2013

See at: ijcai.org Open Access | CNR ExploRA Open Access


2013 Contribution to book Restricted

The discovery of discrimination
Pedreschi D., Ruggieri S., Turini F.
Discrimination discovery from data consists in the extraction of discriminatory situations and practices hidden in a large amount of historical decision records.We discuss the challenging problems in discrimination discovery, and present, in a unified form, a framework based on classification rules extraction and filtering on the basis of legally-grounded interestingness measures. The framework is implemented in the publicly available DCUBE tool. As a running example, we use a public dataset on credit scoring.Source: Discrimination and Privacy in the Information Society. Data Mining and Profiling in Large Databases., edited by Bart Custers, Toon Calders, Bart Schermer, Tal Zarsky, pp. 91–108. Berlin Heidelberg: Springer, 2013
DOI: 10.1007/978-3-642-30487-3_5

See at: academic.microsoft.com Restricted | arpi.unipi.it Restricted | core.ac.uk Restricted | dblp.uni-trier.de Restricted | doi.org Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted | www.springerlink.com Restricted


2012 Journal article Restricted

A complexity perspective on entailment of parameterized linear constraints
Eirinakis P., Ruggieri S., Subramani K., Wojciechowski P.
Extending linear constraints by admitting parameters allows for more abstract problem modeling and reasoning. A lot of focus has been given to conducting research that demonstrates the usefulness of parameterized linear constraints and implementing tools that utilize their modeling strength. However, there is no approach that considers basic theoretical tools related to such constraints that allow for reasoning over them. Hence, in this paper we introduce satisf iability with respect to polyhedral sets and entailment for the class of parameterized linear constraints. In order to study the computational complexities of these problems, we relate them to classes of quantified linear implications. The problem of satisfiability with respect to polyhedral sets is then shown to be co-NP hard. The entailment problem is also shown to be co-NP hard in its general form. Nevertheless, we characterize some subclasses for which this problem is in P. Furthermore, we examine a weakening and a strengthening extension of the entailment problem. The weak entailment problem is proved to be NP complete. On the other hand, the strong entailment problem is shown to be co-NP hard.Source: Constraints (Dordrecht. Online) 17 (2012): 461–487. doi:10.1007/s10601-012-9127-x
DOI: 10.1007/s10601-012-9127-x

See at: Constraints Restricted | Constraints Restricted | Constraints Restricted | link.springer.com Restricted | Constraints Restricted | Constraints Restricted | Constraints Restricted | Constraints Restricted | CNR ExploRA Restricted | Constraints Restricted


2012 Conference article Open Access OPEN

Discovering gender discrimination in project funding
Romei A., Ruggieri S., Turini F.
The selection of projects for funding can hide discriminatory decisions. We present a case study investigating gender discrimination in a dataset of scientific research proposals submitted to an Italian national call. The method for the analysis relies on a data mining classification strategy that is inspired by a legal methodology for proving evidence of social discrimination against protected-by-law groups.Source: IEEE, 12th International Conference on Data Mining Workshops, ICDMW 2012., Brussels, Belgium, 10 December 2012
DOI: 10.1109/icdmw.2012.39

See at: www.di.unipi.it Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | doi.org Restricted | ieeexplore.ieee.org Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | www.di.unipi.it Restricted | xplorestaging.ieee.org Restricted


2012 Conference article Restricted

Computational complexities of inclusion queries over polyhedral sets
Eirinakis P., Ruggieri S., Subramani K., Wojciechowski P.
In this paper we discuss the computational complexities of procedures for inclusion queries over polyhedral sets. The polyhedral sets that we consider occur in a wide range of applications, ranging from logistics to program verification. The goal of our study is to establish boundaries between hard and easy problems in this context.Source: International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, FL, USA, 9-11 January 2012

See at: CNR ExploRA Restricted | www.cs.uic.edu Restricted


2012 Conference article Restricted

Deciding membership in a class of polyhedra
Ruggieri S.
Parameterized linear systems allow for modelling and reasoning over classes of polyhedra. Collections of squares, rectangles, polytopes, and so on can readily be defined by means of linear systems with parameters in constant terms. In this paper, we consider the membership problem of deciding whether a given polyhedron belongs to the class defined by a parameterized linear system. As an example, we are interested in questions such as: "does a given polytope belong to the class of hypercubes?" We show that the membership problem is NP-complete, even when restricting to the 2-dimensional plane. Despite the negative result, the constructive proof allows us to devise a concise decision procedure using constraint logic programming over the reals, namely CLP(R), which searches for a characterization of all instances of a parameterized system that are equivalent to a given polyhedron.Source: 20th European Conference on Artificial Intelligence, pp. 702–707, Montpellier, Francia, 27-31 agosto 2012
DOI: 10.3233/978-1-61499-098-7-702

See at: ebooks.iospress.nl Restricted | CNR ExploRA Restricted