Handle with Care: Lessons for Data Science from Black Female Scholars

By Inioluwa Deborah Raji. 2020

5 citations recorded by the source corpus.

The contribution of Black female scholars to our understanding of data and their limits of representation hint at a more empathetic vision for data science that we should all learn from.

Subfields: Artificial Intelligence, Safety Research, Statistics and Probability, AI Ethics, Fairness & Society

How researchers see this paper

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

In Artificial Intelligence

1. Iason Gabriel
2. Po-Sen Huang
3. Jonathan Uesato
4. William S. Isaac
5. John F. J. Mellor
6. Borja Balle
7. Geoffrey Irving
8. Amelia Glaese

In Safety Research

1. William S. Isaac
2. Iason Gabriel
3. Geoffrey Irving
4. Myra Cheng
5. T. Stepleton
6. Zachary Kenton
7. Atoosa Kasirzadeh
8. Laura Weidinger

In Statistics and Probability

1. William S. Isaac
2. Borja Balle
3. Geoffrey Irving
4. Zachary Kenton
5. Abeba Birhane

In AI Ethics, Fairness & Society

1. William S. Isaac
2. Iason Gabriel
3. Borja Balle
4. T. Stepleton
5. Atoosa Kasirzadeh
6. Abeba Birhane

Citations in the corpus

• Ethical and social risks of harm from Language Models (2021, background): “The groups that are traditionally underrepresented in training data are often disadvantaged groups: they are also referred to as the ‘undersampled majority’ (Raji, 2020).”
• Taxonomy of Risks posed by Language Models (2022, background): “One reason for this is the underrepresentation of certain groups and languages in training corpora, which often disproportionately affects communities who are marginalised, excluded, or less frequently recorded, also referred to as the ”undersampled majority” [150].”