Recommender Systems and Algorithmic Hate

Author:

Smith Jessie J.1,Jayne Lucia1,Burke Robin2

Affiliation:

1. Information Science, University of Colorado Boulder, United States

2. Department of Information Science, University of Colorado, Boulder, United States

Funder

National Science Foundation

Publisher

ACM

Reference52 articles.

1. Recommender Systems as Multistakeholder Environments

2. Jack Bandy and Nicholas Diakopoulos . 2021. Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News . Social Media + Society 7, 3 ( 2021 ), 1–17. Jack Bandy and Nicholas Diakopoulos. 2021. Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News. Social Media + Society 7, 3 (2021), 1–17.

3. Solon Barocas , Kate Crawford , Aaron Shapiro , and Hanna Wallach . 2017 . The problem with bias: Allocative versus representational harms in machine learning . In 9th Annual conference of the special interest group for computing, information and society. Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017. The problem with bias: Allocative versus representational harms in machine learning. In 9th Annual conference of the special interest group for computing, information and society.

4. Garfield Benjamin . 2022 . # FuckTheAlgorithm: algorithmic imaginaries and political resistance . In ACM Conference on Fairness, Accountability, and Transparency 2022. Garfield Benjamin. 2022. # FuckTheAlgorithm: algorithmic imaginaries and political resistance. In ACM Conference on Fairness, Accountability, and Transparency 2022.

5. Taina Bucher . 2017. The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information, communication & society 20, 1 ( 2017 ), 30–44. Taina Bucher. 2017. The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information, communication & society 20, 1 (2017), 30–44.

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