Data, Responsibly: Fairness, Neutrality and Transparency in Data Analysis

Julia Stoyanovich 1 Serge Abiteboul 2, 3 Gerome Miklau 4
3 DAHU - Verification in databases
CNRS - Centre National de la Recherche Scientifique : UMR8643, Inria Saclay - Ile de France, ENS Cachan - École normale supérieure - Cachan, LSV - Laboratoire Spécification et Vérification [Cachan]
Abstract : Big data technology holds incredible promise of improving people's lives, accelerating scientific discovery and innovation , and bringing about positive societal change. Yet, if not used responsibly, this technology can propel economic inequality , destabilize global markets and affirm systemic bias. While the potential benefits of big data are well-accepted, the importance of using these techniques in a fair and transparent manner is rarely considered. The primary goal of this tutorial is to draw the attention of the data management community to the important emerging subject of responsible data management and analysis. We will offer our perspective on the issue, will give an overview of existing technical work, primarily from the data mining and algorithms communities, and will motivate future research directions.
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Julia Stoyanovich, Serge Abiteboul, Gerome Miklau. Data, Responsibly: Fairness, Neutrality and Transparency in Data Analysis. International Conference on Extending Database Technology, Mar 2016, Bordeaux, France. ⟨hal-01290695⟩

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