I'm still confused on the semantics here. My data in this case is broadly representative of the real world input - so what's wrong? This is where I'm confused

I'm still confused on the semantics here. My data in this case is broadly representative of the real world input - so what's wrong? This is where I'm confused on the general idea of "algorithmic bias". Any maximally optimized classifier over the population may perform worse on any minority group (broadly defined). Representative data is necessary to achieve highest efficiency (as otherwise you will underperform in the real world), but it doesn't solve the problem of say relatively reduced recall of a minority group (which is often discussed in these posts). In fact, given that groupings are arbitrary, I don't think this is a solvable problem.