Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements-Reference-Cited by-同舟云学术

Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements

Published:2023-10-30 Issue: Volume: Page:
ISSN:
Container-title:Equity and Access in Algorithms, Mechanisms, and Optimization
language:
Short-container-title:

Author:

Molamohammadi Maryam¹^ORCID,Taïk Afaf²^ORCID,Le Roux Nicolas³^ORCID,Farnadi Golnoosh⁴^ORCID

Affiliation:

1. Mila, Canada

2. Universite de Montreal, Canada and Mila, Canada

3. Mila, Canada and Microsoft Research, Canada

4. Computer Science, McGill university, Canada and Mila, Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3617694.3623256

Reference111 articles.

1. Narratives and Counternarratives on Data Sharing in Africa

2. Alekh Agarwal , Alina Beygelzimer , Miroslav Dudík , John Langford , and Hanna Wallach . 2018 . A reductions approach to fair classification . In International Conference on Machine Learning. PMLR, 60–69 . Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. 2018. A reductions approach to fair classification. In International Conference on Machine Learning. PMLR, 60–69.

3. Dario Amodei and Danny Hernandez . 2018. AI and compute , 2018 . URL https://openai. com/blog/ai-and-compute 4 (2018). Dario Amodei and Danny Hernandez. 2018. AI and compute, 2018. URL https://openai. com/blog/ai-and-compute 4 (2018).

4. Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2016. Machine bias . In Ethics of Data and Analytics . Auerbach Publications , 254–264. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. In Ethics of Data and Analytics. Auerbach Publications, 254–264.

5. FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive