CausalFairML via RPID

doi:doi:10.57702/nkwmzleo

CausalFairML via RPID

A decision can be defined as fair if equal individuals are treated equally and unequals are treated unequally. Adopting this definition, the task of designing machine learning (ML) models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes: Following a recent proposal, we define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world, where the protected attributes have no (direct or indirect) causal effect on the target.

BibTex:

@dataset{Ludwig_Bothmann_and_Susanne_Dandl_and_Michael_Schomaker_2025,
    abstract = {A decision can be defined as fair if equal individuals are treated equally and unequals are treated unequally. Adopting this definition, the task of designing machine learning (ML) models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes: Following a recent proposal, we define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world, where the protected attributes have no (direct or indirect) causal effect on the target.},
    author = {Ludwig Bothmann and Susanne Dandl and Michael Schomaker},
    doi = {10.57702/nkwmzleo},
    institution = {No Organization},
    keyword = {'Causal Fair Machine Learning', 'Fairness in Machine Learning', 'Rank-Preserving Interventional Distributions'},
    month = {jan},
    publisher = {TIB},
    title = {CausalFairML via RPID},
    url = {https://service.tib.eu/ldmservice/dataset/causalfairml-via-rpid},
    year = {2025}
}