Think Again: Big Data

34 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.

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “Entire communities may be missing or misrepresented in datasets, exacerbated by digital divides, leading to wrong conclusions [30, 52, 53, 119] and residual unfairness [103].”
Social Bias in Elicited Natural Language Inferences (2017, background): “High-profile cases of overgeneralization in the public sphere abound (Crawford, 2013; Crawford, 2016; Barocas and Selbst, 2016).”
An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence (2017, background): “The demographic breakdown of smartphone users at the time would have led to blind spots, causing some communities to be underserved (Crawford, 2013).”
Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making (2018, unknown): “Datasets used to train models run the risk of having ‘dark spots’ [30] where communities and classes of individuals are overlooked.”
Bias in machine learning - what is it good for? (2020, background): “An opposite example demonstrates how the big data era with its automatic data gathering can create ‘dark zones or shadows where some citizens and communities are overlooked’ [12].”