Data and algorithmic bias in the web

By Ricardo A. Baeza-Yates. 2016

81 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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

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In Safety, Robustness & Interpretability

Citations in the corpus

Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses (2020, background): “While we have not performed a comprehensive study, the observations and claims open an intriguing research problem (Baeza-Yates, 2016).”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “Previous work has investigated how many algorithms rely on data that is imbued with societal biases and explored how to address this issue [3, 12, 50, 56].”
A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2018, background): “Other resources we recommend: books [29, 33], talks and tutorials [1, 9, 15, 36], and several overview papers [4, 25, 31, 35].”
Biases in Large Language Models: Origins, Inventory, and Discussion (2023, background): “Yet, we also need to keep in mind that, in the words of Baeza-Yates, łthe output quality of any algorithm is a function of the quality of the data that it usesž [6].”
Advances, challenges and opportunities in creating data for trustworthy AI (2022, background): “And companies can hoard expensive private datasets under restrictive licenses 94 , leaving academic researchers to scrape the Internet for lower-quality and potentially biased data 95 .”
Setting the future of digital and social media marketing research: Perspectives and research propositions (2020, background): “Baeza-Yates (2016) have stressed the importance of user context to avoid these biases in recommendation and personalization systems.”