If ML is based on human data, can it outperform humans? Yes. HINT: scale.

{Search, Recommendation, Ads} systems are something we all use today. These ML systems greatly outperform humans. Most of these successful ML systems rely on human-generated data to train. For example, Google looks at what users clicked to train their relevance models. Facebook uses which ads get the most dwell time to learn what types of ads to show next time. Reddit uses user upvote/downvote data and user clicks to learn which posts to boost.

Peter Norvig who ran search quality at Google stated that getting above 80% recall for these systems was quite good [reference: https://qr.ae/pryCgm]. The average human performance on most of these tasks is around 90%. Most of these systems are outperforming humans even though they are not getting high enough recall on individual samples.

Why?

Since these things operate at scale, not every suggestion has to be perfect. The user can ignore the bad suggestions. Furthermore, in the more advanced versions of these systems (i.e. personalized versions of these systems), one could improve recall by simply learning about the user.

Most ML systems that have a defined goal and scale will be able to outperform humans. They will outperform humans even if they use human-generated data. This will only be true if they perform at scale.

A more interesting version of your question would be can we build a single system that can outperform all humans in all tasks? This is the AGI question.