Semantic Scholar Open Access 2019 259 sitasi

Fairness in Deep Learning: A Computational Perspective

Mengnan Du Fan Yang Na Zou Xia Hu

Lihat Sumber DOI

Abstrak

Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Specifically, we show that interpretability can serve as a useful ingredient to diagnose the reasons that lead to algorithmic discrimination. We also discuss fairness mitigation approaches categorized according to three stages of deep learning life-cycle, aiming to push forward the area of fairness in deep learning and build genuinely fair and reliable deep learning systems.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (4)

Mengnan Du

Fan Yang

Na Zou

Xia Hu

Format Sitasi

APA MLA BibTeX

Du, M., Yang, F., Zou, N., Hu, X. (2019). Fairness in Deep Learning: A Computational Perspective. https://doi.org/10.1109/MIS.2020.3000681

Akses Cepat

Lihat di Sumber doi.org/10.1109/MIS.2020.3000681

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 259×
Sumber Database: Semantic Scholar
DOI: 10.1109/MIS.2020.3000681
Akses: Open Access ✓