Transparency and Proportionality in Post-Processing Algorithmic Bias Correction

Debiasing can create new problems: The paper shows that techniques used to fix algorithmic bias in post-processing (after a system makes predictions) can sometimes introduce new forms of unfairness or make existing inequalities worse, meaning well-intentioned fixes don't always work as expected.

Proportionality matters as much as fairness: Traditional fairness metrics alone are insufficient; the paper introduces new measures to assess whether bias corrections are applied proportionally across different groups, ensuring that fixing bias for one group doesn't disproportionately penalize another.

Transparency requires explaining who gets affected: The proposed methodology helps practitioners understand and explain specifically how debiasing strategies affect each demographic group differently, moving beyond aggregate statistics to show concrete impacts on individuals.

Practitioners need practical tools for accountability: The paper provides a concrete methodology and set of metrics that organizations can use during the post-processing stage to evaluate whether their debiasing approach is truly fair, enabling them to choose between different bias mitigation strategies based on evidence.

Deeper analysis prevents unintended discrimination: By examining the "flips" (prediction changes) made during debiasing, organizations can catch cases where correcting bias for one group inadvertently creates new disparities, allowing them to make more informed decisions about which debiasing approaches to adopt.