Diversity in Big Data: A Review

153 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Strongly linked to the concept of fairness, much attention has been lately devoted to the concept of data diversity , which essentially refers to the capability of an algorithmic model to ensure that all diﬀerent types of objects are represented in its output [403] .”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “less useful than a collection of diverse items [19].”
A Nutritional Label for Rankings (2018, methodology): “The Diversity widget shows diversity with respect to a set of demographic categories of individuals, or a set of categorical attributes of other kinds of items [2].”
Diversity and Inclusion Metrics in Subset Selection (2020, background): “to the idea of diversity discussed here [2, 8, 13, 32, 36].”
Designing Fair Ranking Schemes (2017, background): “General background on diversity and a connection to fairness are provided in [13].”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, background): “Pivotal moments such as the choice for a particular algorithm [e.g. 56, p. 549], and other design decisions [101] such as the weighting of factors [40, p. 17], or balancing of ‘fairness’ [59] deeply influence the system.”