Computer ScienceNature Machine Intelligence
On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare
S. Mittal, K. Thakral, et al.
This study highlights the critical need for responsible machine learning datasets, focusing on fairness, privacy, and compliance with regulations. Conducted by Surbhi Mittal, Kartik Thakral, Richa Singh, Mayank Vatsa, Tamar Glaser, Cristian Canton Ferrer, and Tal Hassner, it uncovers widespread issues within computer vision datasets, especially in biometrics and healthcare, and proposes a new evaluation rubric.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Medicine and Health
A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders
A. Zadka, N. Rabin, et al.
Engineering and Technology
Exploiting redundancy in large materials datasets for efficient machine learning with less data
K. Li, D. Persaud, et al.
Linguistics and Languages
Stylistic and linguistic variations in compliments: an empirical analysis of children's gender schema development with machine learning algorithms
X. Liao and Y. Zhang
Humanities
Decoding violence against women: analysing harassment in Middle Eastern literature with machine learning and sentiment analysis
H. Q. Low, P. Keikhosrokiani, et al.
