Multi-objective Few-shot Learning for Fair Classification-Reference-Cited by-同舟云学术

Multi-objective Few-shot Learning for Fair Classification

Published:2021-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th ACM International Conference on Information & Knowledge Management
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Author:

Mondal Ishani¹,Sen Procheta²,Ganguly Debasis³

Affiliation:

1. Microsoft Resarch, Bangalore, India

2. Dublin CIty University, Dublin, Ireland

3. University of Glasgow, Glasgow, United Kingdom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3459637.3482146

Reference14 articles.

1. Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. href https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias there's software used across the country to predict future criminals. and it's biased against blacks. Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. href https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias there's software used across the country to predict future criminals. and it's biased against blacks.

2. Bias Analysis and Mitigation in the Evaluation of Authorship Verification

3. Language (Technology) is Power: A Critical Survey of “Bias” in NLP

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1. 'Choose your Data Wisely': Active Learning based Selection with Multi-Objective Optimisation for Mitigating Stereotypes;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

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3. Optimal Architecture Discovery for Physics-Informed Neural Networks;Advances in Artificial Intelligence – IBERAMIA 2022;2022