Literature DB >> 28632443

Diversity in Big Data: A Review.

Marina Drosou1, H V Jagadish2, Evaggelia Pitoura1, Julia Stoyanovich3.   

Abstract

Big data technology offers unprecedented opportunities to society as a whole and also to its individual members. At the same time, this technology poses significant risks to those it overlooks. In this article, we give an overview of recent technical work on diversity, particularly in selection tasks, discuss connections between diversity and fairness, and identify promising directions for future work that will position diversity as an important component of a data-responsible society. We argue that diversity should come to the forefront of our discourse, for reasons that are both ethical-to mitigate the risks of exclusion-and utilitarian, to enable more powerful, accurate, and engaging data analysis and use.

Entities:  

Keywords:  data; diversity; empirical studies; models and algorithms; responsibly

Mesh:

Year:  2017        PMID: 28632443     DOI: 10.1089/big.2016.0054

Source DB:  PubMed          Journal:  Big Data        ISSN: 2167-6461            Impact factor:   2.128


  2 in total

1.  Inequality and inequity in network-based ranking and recommendation algorithms.

Authors:  Lisette Espín-Noboa; Claudia Wagner; Markus Strohmaier; Fariba Karimi
Journal:  Sci Rep       Date:  2022-02-07       Impact factor: 4.379

2.  Standard operating procedures for biobank in oncology.

Authors:  Giuseppina Bonizzi; Lorenzo Zattoni; Maria Capra; Cristina Cassi; Giulio Taliento; Mariia Ivanova; Elena Guerini-Rocco; Marzia Fumagalli; Massimo Monturano; Adriana Albini; Giuseppe Viale; Roberto Orecchia; Nicola Fusco
Journal:  Front Mol Biosci       Date:  2022-08-26
  2 in total

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