JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data Scientists

Author:

Harrison Galen1ORCID,Bryson Kevin2ORCID,Bamba Ahmad Emmanuel Balla2ORCID,Dovichi Luca2ORCID,Binion Aleksander Herrmann2ORCID,Borem Arthur2ORCID,Ur Blase2ORCID

Affiliation:

1. University of Virginia, United States and University of Chicago, United States

2. University of Chicago, United States

Funder

National Science Foundation

Publisher

ACM

Reference56 articles.

1. Sara Alspaugh, Nava Zokaei, Andrea Liu, Cindy Jin, and Marti A. Hearst. 2019. Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices. IEEE Transactions on Visualization and Computer Graphics 25, 1 (2019).

2. Julia Angwin and Jeff Larson. 2016. Machine Bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

3. “☑ Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms

4. Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2023. Fairness and Machine Learning: Limitations and Opportunities. The MIT Press.

5. Symphony: Composing Interactive Interfaces for Machine Learning

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