This overview addresses this issue by providing a gentle introduction to RSs, discussing their causes and consequences in intuitive terms, and details methods for dealing with RSs, including mitigation and awareness strategies, and maps their benefits and limitations.
E. Marconato, Samuele Bortolotti, Emile van Krieken et al.· Journal of Artificial Intell...· 1 citation
It is demonstrated that standard LtD strategies show class-dependent sampling bias in classification tasks in practice, and thus may disproportionately defer the minority classes when applied to imbalanced datasets, and that such asymmetries in task delegation may trigger human biases, ultimately leading to poorer downstream decision making.
Dario Pesenti, A. Bogani, Stefano Teso et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.