Tag
This paper argues that aggregating moral evaluations for AI value alignment must account for contextual factors, showing that ignoring context can lead to violations of the weak Pareto principle, analogous to Simpson's paradox.
This academic paper identifies and characterizes Simpson's paradox in behavioral curve modeling, demonstrating how aggregation systematically distorts parametric estimates of user dynamics due to survival bias. The authors validate this distortion across datasets like Goodreads and Amazon Electronics and propose hierarchical peak estimation methods to mitigate the issue.
This paper addresses the degradation of likelihood-based machine-generated text detectors by identifying a Simpson's paradox in token-score aggregation. It proposes a learned local calibration step that significantly improves detection performance across various models and datasets.