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Discrimination-aware data mining
TLDR
This 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, and an empirical assessment of the results on the German credit dataset.
Local Rule-Based Explanations of Black Box Decision Systems
TLDR
This paper proposes LORE, an agnostic method able to provide interpretable and faithful explanations for black box outcome explanation, and shows that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box.
k-NN as an implementation of situation testing for discrimination discovery and prevention
TLDR
This paper tackles the problems of discrimination discovery and prevention from a dataset of historical decisions by adopting a variant of k-NN classification, which overcomes legal weaknesses and technical limitations of existing proposals.
A multidisciplinary survey on discrimination analysis
TLDR
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.
Data mining for discrimination discovery
TLDR
This article formalizes 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 and proposes two inference models and provides automatic procedures for their implementation.
Measuring Discrimination in Socially-Sensitive Decision Records
TLDR
This work tackles the problem of determining, given a dataset of historical decision records, a precise measure of the degree of discrimination suffered by a given group in a given context with respect to the decision, and introduces a collection of quantitative measures of discrimination.
Integrating induction and deduction for finding evidence of discrimination
TLDR
An implementation, called LP2DD, of the overall reference model that integrates induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools is presented.
Bias in data‐driven artificial intelligence systems—An introductory survey
TLDR
A broad multidisciplinary overview of the area of bias in AI systems is provided, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame.
Preprocessing and Mining Web Log Data for Web Personalization
TLDR
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 by means of data and web mining techniques are described.
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