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The Resume Paradox: Greater Language Differences, Smaller Pay Gaps
This suggests that females' resumes that are semantically similar to males' resumes may have greater wage parity.
Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance
Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements.
A Sound and Complete Algorithm for Learning Causal Models from Relational Data
However, there is no equivalent complete algorithm for learning the structure of relational models, a more expressive generalization of Bayesian networks.
Reasoning about Independence in Probabilistic Models of Relational Data
We extend the theory of d-separation to cases in which data instances are not independent and identically distributed.
Identifying Independence in Relational Models
The rules of d-separation provide a framework for deriving conditional independence facts from model structure.
