Semantic Scholar Open Access 2018 515 sitasi

Delayed Impact of Fair Machine Learning

Lydia T. Liu Sarah Dean Esther Rolf Max Simchowitz Moritz Hardt

Lihat Sumber DOI

Abstrak

Static classification has been the predominant focus of the study of fairness in machine learning. While most models do not consider how decisions change populations over time, it is conventional wisdom that fairness criteria promote the long-term well-being of groups they aim to protect. This work studies the interaction of static fairness criteria with temporal indicators of well-being. We show a simple one-step feedback model in which common criteria do not generally promote improvement over time, and may in fact cause harm. Our results highlight the importance of temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.

Topik & Kata Kunci

Mathematics Computer Science

Penulis (5)

Lydia T. Liu

Sarah Dean

Esther Rolf

Max Simchowitz

Moritz Hardt

Format Sitasi

APA MLA BibTeX

Liu, L.T., Dean, S., Rolf, E., Simchowitz, M., Hardt, M. (2018). Delayed Impact of Fair Machine Learning. https://doi.org/10.24963/ijcai.2019/862

Akses Cepat

Lihat di Sumber doi.org/10.24963/ijcai.2019/862

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 515×
Sumber Database: Semantic Scholar
DOI: 10.24963/ijcai.2019/862
Akses: Open Access ✓