In this work we propose FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a novel solution to detect biases and discover discrimination in datasets, that exploits the notion of Functional Dependency, a particular type of constraint on the data.
Nov 23, 2022 � The aim of the E-FAIR-DB system is to detect bias in datasets with the help of Approximate Conditional Functional Dependencies and mitigate this�...
In this demo, we propose FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a system that exploiting the notion of Functional Dependency, a particular�...
Aug 4, 2022 � Our tool can identify, through the mined dependencies, the attributes of the database that encompass discrimination (e.g. gender, ethnicity or�...
FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a novel solution to detect biases and discover discrimination in datasets, that exploits the notion�...
In this work we present FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a novel framework to detect biases and discover discrimination in datasets.
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In this demo, we propose FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a system that exploiting the notion of Functional Dependency, a particular�...
E-FAIR-DB is proposed, a novel solution that, exploiting the notion of Functional Dependency—a type of data constraint—aims at restoring data equity by�...
In this work we propose FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a novel solution to detect biases and discover discrimination in datasets,�...
In this work we propose E-FAIR-DB, a novel solution that, exploiting the notion of Functional Dependency - a type of data constraint - aims at restoring data�...
