  The protection of sensitive data becomes more vital, as data increases in
value and potency. Furthermore, the pressure increases from regulators and
society on model developers to make their Artificial Intelligence (AI) models
non-discriminatory. To boot, there is a need for interpretable, transparent AI
models for high-stakes tasks. In general, measuring the fairness of any AI
model requires the sensitive attributes of the individuals in the dataset, thus
raising privacy concerns. In this work, the trade-offs between fairness,
privacy and interpretability are further explored. We specifically examine the
Statistical Parity (SP) of Decision Trees (DTs) with Differential Privacy (DP),
that are each popular methods in their respective subfield. We propose a novel
method, dubbed Privacy-Aware Fairness Estimation of Rules (PAFER), that can
estimate SP in a DP-aware manner for DTs. DP, making use of a third-party legal
entity that securely holds this sensitive data, guarantees privacy by adding
noise to the sensitive data. We experimentally compare several DP mechanisms.
We show that using the Laplacian mechanism, the method is able to estimate SP
with low error while guaranteeing the privacy of the individuals in the dataset
with high certainty. We further show experimentally and theoretically that the
method performs better for DTs that humans generally find easier to interpret.

Machine Learning;Cryptography and Security;Computers and Society