Bias in machine learning - what is it good for?

By Thomas Hellström, Virginia Dignum, Suna Bensch. 2020

88 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.

1. Ryan Shaun Joazeiro de Baker
2. Patrick Hall
3. Andrew Burt
4. Reva Schwartz
5. Kristen Greene
6. Apostol T. Vassilev
7. Aaron Hawn
8. Lori A. Perine

Citations in the corpus

• Algorithmic Bias in Education (2021, background): “Common sources of bias have been outlined in several taxonomies and related to stages in the machine learning lifecycle (Barocas et al., 2019; Hellström et al., 2020; Mehrabi et al., 2019; Silva & Kenney, 2018; Suresh & Guttag, 2020).”
• Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, methodology): “If the output is biased in any way, this bias may be inherited by systems using the output as input to learn other models [83].”