Nondiscriminating Dropout Prediction beyond Algorithmic Fairness: Ensuring Coverage in Preprocessing Pipelines-Reference-Cited by-同舟云学术

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Nondiscriminating Dropout Prediction beyond Algorithmic Fairness: Ensuring Coverage in Preprocessing Pipelines

Published:2022-12-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Big Data (Big Data)
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Author:

Accinelli Chiara¹,Catania Barbara¹,Guerrini Giovanna¹

Affiliation:

1. University of Genoa,DIBRIS,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020717.pdf?arnumber=10020717

Reference35 articles.

1. Towards equalised odds as fairness metric in academic performance prediction;dunkelau;arXiv preprint arXiv 2209 14670,2022

2. Fairness in ranking: A survey;zehlike;CoRR abs/2103 14000,2021

3. Diversity in Big Data: A Review

4. Towards accurate and fair prediction of college success: Evaluating different sources of student data;yu;International Educational Data Mining Society,2020

5. Towards fair educational data mining: A case study on detecting at-risk students;hu;International Educational Data Mining Society,2020

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1. Mitigating Representation Bias in Data Transformations: A Constraint-based Optimization Approach;2023 IEEE International Conference on Big Data (BigData);2023-12-15

2. Embracing Diversity and Inclusion: A Decolonial Approach to Urban Computing;2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA);2023-12-04

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