RUNNER: Responsible UNfair NEuron Repair for Enhancing Deep Neural Network Fairness

Author:

Li Tianlin1ORCID,Cao Yue1ORCID,Zhang Jian1ORCID,Zhao Shiqian1ORCID,Huang Yihao1ORCID,Liu Aishan2ORCID,Guo Qing3ORCID,Liu Yang1ORCID

Affiliation:

1. Nanyang Technological University, Singapore, Singapore

2. Beihang University, Beijing, China

3. ASTAR, Singapore, China

Funder

National Research Foundation, Singapore

Publisher

ACM

Reference70 articles.

1. Tameem Adel Isabel Valera Zoubin Ghahramani and Adrian Weller. 2019. One-Network Adversarial Fairness. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu Hawaii USA) (AAAI'19/IAAI'19/EAAI'19). AAAI Press Article 298 9 pages. 10.1609/aaai.v33i01.33012412

2. Md Zahangir Alom, Tarek M Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S Awwal, and Vijayan K Asari. 2018. The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164 (2018).

3. Mohsan Alvi, Andrew Zisserman, and Christoffer Nellåker. 2018. Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops. 0--0.

4. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

5. Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, and Seong Joon Oh. 2020. Learning de-biased representations with biased representations. In International Conference on Machine Learning. PMLR, 528--539.

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