"By comparing the decisions output by diverse settings, we find that ML algorithms can mitigate both the preference-based bias and the belief-based bias, while the effects vary for new and repeated applicants. Based on our findings, we propose a two-step human-AI collaboration framework for practitioners to reduce decision bias most effectively."
top of page
Rechercher
Posts récents
Voir tout“As analysts are primary recipients of these reports, we investigate whether and how analyst forecast properties have changed following...
This study proposes a new method for detecting insider trading. The method combines principal component analysis (PCA) with random forest...
Cyber risk classifications often fail in out-of-sample forecasting despite their in-sample fit. Dynamic, impact-based classifiers...
bottom of page
Comments