Data and Algorithmic Bias in the Web
summary
Covers bias in web-scale data and algorithms, focusing on how redundancy, spam, and algorithmic design introduce selection, presentation and interaction biases in search and recommendation systems. Discusses concrete relationships between these biases and issues like data sparsity, novelty, and privacy, and emphasizes using user context to mitigate them.
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