Teaching Responsible Data Science-Reference-Cited by-同舟云学术

Teaching Responsible Data Science

Published:2022-06-12 Issue: Volume: Page:
ISSN:
Container-title:1st International Workshop on Data Systems Education
language:
Short-container-title:

Author:

Stoyanovich Julia¹

Affiliation:

1. Computer Science & Engineering, Tandon School of Engineering, New York University, USA and Center for Data Science, New York University, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3531072.3535318

Reference43 articles.

1. Muhammad Ali , Piotr Sapiezynski , Miranda Bogen , Aleksandra Korolova , Alan Mislove , and Aaron Rieke . 2019 . Discrimination through Optimization: How Facebook’s Ad Delivery Can Lead to Biased Outcomes. Proc. ACM Hum. Comput. Interact. 3, CSCW (2019), 199:1–199:30. https://doi.org/10.1145/3359301 10.1145/3359301 Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019. Discrimination through Optimization: How Facebook’s Ad Delivery Can Lead to Biased Outcomes. Proc. ACM Hum. Comput. Interact. 3, CSCW (2019), 199:1–199:30. https://doi.org/10.1145/3359301

2. Responsibly Comic Series 2;Khan Falaah Arif,2021

3. Responsibly Comic Series 1;Khan Falaah Arif,2020

4. A snapshot of the frontiers of fairness in machine learning

5. Amit Datta , Anupam Datta , Jael Makagon , Deirdre K. Mulligan , and Michael Carl Tschantz . 2018 . Discrimination in Online Personalization: A Multidisciplinary Inquiry. In Conference on Fairness, Accountability and Transparency, FAT 2018 , 23-24 February 2018, New York, NY, USA. 20–34. http://proceedings.mlr.press/v81/datta18a.html Amit Datta, Anupam Datta, Jael Makagon, Deirdre K. Mulligan, and Michael Carl Tschantz. 2018. Discrimination in Online Personalization: A Multidisciplinary Inquiry. In Conference on Fairness, Accountability and Transparency, FAT 2018, 23-24 February 2018, New York, NY, USA. 20–34. http://proceedings.mlr.press/v81/datta18a.html