Suppose a person says something that the codec interprets differently. Perhaps they have one of the many ever evolving accents that almost certainly were not and absolutely could not possibly be included in the training set (ongoing vowel shifts might be a big cause of this). The algorithm removes the ambiguity, but the listener can't tell because they hear themselves through their own sense of hearing. Assume the user has somehow overcome the odd psychological effects that come hearing the computer generated audio played back, if that audio is mixed with what the person is already hearing, it's likely they still won't notice because they still hear themselves. They would have to listen to a recording some time later and detect that the recording doesn't match up with what they thought they said... which happens all the time because memory is incredibly lossy and maleable.
Most of the time, it won't matter. People have context (assuming they're actually listening, which is a whole other tier of hearing what you expect to hear) and people pick the wrong word or pronounce things incorrectly (as in not recognizable to the listener as what the speaker intended) all the time. But it'll be really hard to know that the recording doesn't record what was actually said. You need to record a local accurate copy, the receiver's processed copy, and know to look for it in what will likely be many hours of audio. It's also possible that "the algorithm said that" will be a common enough argument due to other factors (incorrect memory and increasing awareness of ML-based algorithms) that it'll out number the cases where it really happens.