selection bias
Selection bias occurs when the data used to train a model is not representative of the underlying population, leading to skewed results. Addressing selection bias is critical for ensuring model fairness, reliability, and generalizability in AI applications.
- Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
- On the Value of Cross-Modal Misalignment in Multimodal Representation Learning
- Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference
- Quantifying Uncertainty in the Presence of Distribution Shifts
- Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
- Unveiling Extraneous Sampling Bias with Data Missing-Not-At-Random