Franco Turini

Discrimination-aware data mining

In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Rules extracted from databases by data mining techniques, such as classification or association rules, when used for decision tasks such as benefit or credit approval, can be discriminatory in the above sense. In this paper, the notion of discriminatory classification rules is introduced and studied. Providing a guarantee of non-discrimination is shown to be a non trivial task. A naive approach, like taking away all discriminatory attributes, is shown to be not enough when other background knowledge is available. Our approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge. An empirical assessment of the results on the German credit dataset is also provided.

Preprocessing and Mining Web Log Data for Web Personalization

We describe the web usage mining activities of an on-going project, called ClickWorld, that aims at extracting models of the navigational behaviour of a web site users. The models are inferred from the access logs of a web server by means of data and web mining techniques. The extracted knowledge is deployed to the purpose of offering a personalized and proactive view of the web services to users. We first describe the preprocessing steps on access logs necessary to clean, select and prepare data for knowledge extraction. Then we show two sets of experiments: the first one tries to predict the sex of a user based on the visited web pages, and the second one tries to predict whether a user might be interested in visiting a section of the site.

Data mining for discrimination discovery

In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Discrimination in credit, mortgage, insurance, labor market, and education has been investigated by researchers in economics and human sciences. With the advent of automatic decision support systems, such as credit scoring systems, the ease of data collection opens several challenges to data analysts for the fight against discrimination. In this article, we introduce the problem of discovering discrimination through data mining in a dataset of historical decision records, taken by humans or by automatic systems. We formalize the processes of direct and indirect discrimination discovery by modelling protected-by-law groups and contexts where discrimination occurs in a classification rule based syntax. Basically, classification rules extracted from the dataset allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law groups is formalized by an extension of the lift measure of a classification rule. In direct discrimination, the extracted rules can be directly mined in search of discriminatory contexts. In indirect discrimination, the mining process needs some background knowledge as a further input, for example, census data, that combined with the extracted rules might allow for unveiling contexts of discriminatory decisions. A strategy adopted for combining extracted classification rules with background knowledge is called an inference model. In this article, we propose two inference models and provide automatic procedures for their implementation. An empirical assessment of our results is provided on the German credit dataset and on the PKDD Discovery Challenge 1999 financial dataset.

k-NN as an implementation of situation testing for discrimination discovery and prevention

With the support of the legally-grounded methodology of situation testing, we tackle the problems of discrimination discovery and prevention from a dataset of historical decisions by adopting a variant of k-NN classification. A tuple is labeled as discriminated if we can observe a significant difference of treatment among its neighbors belonging to a protected-by-law group and its neighbors not belonging to it. Discrimination discovery boils down to extracting a classification model from the labeled tuples. Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classifier. The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals.

Modular logic programming

Modularity is a key issue in the design of modern programming languages. When designing modular features for declarative languages in general, and for logic programming languages in particular, the challenge lies in avoiding the superimposition of a complex syntactic and semantic structure over the simple structure of the basic language. The modular framework defined here for logic programming consists of a small number of operations over modules which are (meta-) logically defined and semantically justified in terms of the basic logic programming semantics. The operations enjoy a number of algebraic properties, thus yielding an algebra of modules. Despite its simplicity, the suite of operations is shown capable of capturing the core features of modularization: information hiding, import/export relationships, and construction of module hierarchies. A metalevel implementation and a compilation-oriented implementation of the operations are provided and proved sound with respect to the semantics. The compilation-oriented implementation is based on manipulation of name spaces and provides the basis for an efficient implementation.

A transformational approach to negation in logic programming

Abstract A transformation technique is introduced which, given the Horn-clause definition of a set of predicates p i , synthesizes the definitions of new predicate p i which can be used, under a suitable refutation procedure, to compute the finite failure set of p i . This technique exhibits some computational advantages, such as the possibility of computing nonground negative goals still preserving the capability of producing answers. The refutation procedure, named SLDN refutation, is proved sound and complete with respect to the completed program.

Time-Annotated Sequences for Medical Data Mining

The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While previous work has studied the supervised version of this problem, we study the more challenging case of unsupervised transductive transfer learning, where no labeled data from the target domain are available at training. We describe some current state-of-the-art inductive and transductive approaches and then adapt these models to the problem of transfer learning for protein name extraction. In the process, we introduce a novel maximum entropy based technique, iterative feature transformation (IFT), and show that it achieves comparable performance with state-of-the-art transductive SVMs. We also show how simple relaxations, such as providing additional information like the proportion of positive examples in the test data, can significantly improve the performance of some of the transductive transfer learners.A typical structure of medical data is a sequence of observations of clinical parameters taken at different time moments. In this kind of contexts, the temporal dimension of data is a fundamental variable that should be taken into account in the mining process and returned as part of the extracted knowledge. Therefore, the classical and well established framework of sequential pattern mining is not enough, because it only focuses on the sequentiality of events, without extracting the typical time elapsing between two particular events. Time-annotated sequences (IAS) is a novel mining paradigm that solves this problem. Recently defined in our laboratory [4] together with an efficient algorithm for extracting them, TAS are sequential patterns where each transition between two events is annotated with a typical transition time that is found frequent in the data. In this paper we report a real-world medical case study, in which the TAS mining paradigm is applied to clinical data regarding a set of patients in the follow-up of a liver transplantation. The aim of the data analysis is that of assessing the effectiveness of the extracorporeal photopheresis (ECP) as a therapy to prevent rejection in solid organ transplantation. We believe that this case study does not only show the interestingness of extracting TAS patterns in this particular context but, more ambitiously, it suggests a general methodology for clinical data mining, whenever the time dimension is an important variable of the problem under investigation.

DCUBE: discrimination discovery in databases

Discrimination discovery in databases consists in finding unfair practices against minorities which are hidden in a dataset of historical decisions. The DCUBE system implements the approach of [5], which is based on classification rule extraction and analysis, by centering the analysis phase around an Oracle database. The proposed demonstration guides the audience through the legal issues about discrimination hidden in data, and through several legally-grounded analyses to unveil discriminatory situations. The SIGMOD attendees will freely pose complex discrimination analysis queries over the database of extracted classification rules, once they are presented with the database relational schema, a few ad-hoc functions and procedures, and several snippets of SQL queries for discrimination discovery.

AI*IA 2003: Advances in Artificial Intelligence

Efficiency of the first-order logic proof procedure is a major issue when deduction systems are to be used in real environments, both on their own and as a component of larger systems (e.g., learning systems). Hence, the need of techniques that can perform such a process with reduced time/space requirements (specifically when performing resolution). This paper proposes a new algorithm that is able to return the whole set of solutions to θ-subsumption problems by compactly representing substitutions. It could be exploited when techniques available in the literature are not suitable. Experimental results on its performance are encouraging.

Integrating induction and deduction for finding evidence of discrimination

Automatic Decision Support Systems (DSS) are widely adopted for screening purposes in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. While less arbitrary decisions can potentially be guaranteed, automatic DSS can still be discriminating in the socially negative sense of resulting in unfair or unequal treatment of people. We present a reference model for finding (prima facie) evidence of discrimination in automatic DSS which is driven by a few key legal concepts. First, frequent classification rules are extracted from the set of decisions taken by the DSS over an input pool dataset. Key legal concepts are then used to drive the analysis of the set of classification rules, with the aim of discovering patterns of discrimination. We present an implementation, called LP2DD, of the overall reference model integrating induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools.