  Recent progress towards theoretical interpretability guarantees for AI has
been made with classifiers that are based on interactive proof systems. A
prover selects a certificate from the datapoint and sends it to a verifier who
decides the class. In the context of machine learning, such a certificate can
be a feature that is informative of the class. For a setup with high soundness
and completeness, the exchanged certificates must have a high mutual
information with the true class of the datapoint. However, this guarantee
relies on a bound on the Asymmetric Feature Correlation of the dataset, a
property that so far is difficult to estimate for high-dimensional data. It was
conjectured in W\"aldchen et al. that it is computationally hard to exploit the
AFC, which is what we prove here.
  We consider a malicious prover-verifier duo that aims to exploit the AFC to
achieve high completeness and soundness while using uninformative certificates.
We show that this task is $\mathsf{NP}$-hard and cannot be approximated better
than $\mathcal{O}(m^{1/8 - \epsilon})$, where $m$ is the number of possible
certificates, for $\epsilon>0$ under the Dense-vs-Random conjecture. This is
some evidence that AFC should not prevent the use of interactive classification
for real-world tasks, as it is computationally hard to be exploited.
