Which of the following can cause supervised learning algorithms to make biased predictions? The algorithm is trained on data whose label values are determined by pooling the decisions of multiple human judges. A supervised algorithm is optimized using a highly non-linear objective function. The algorithm is trained using data that omits a critical subgroup of the population. Training set labels are correlated with demographic characteristics. An algorithm is optimized using an objective function that may not match the real world