I'm sorry, but this isn't letting the individuals off the hook, any more than accusing the KKK of being racist isn't letting a particular KKK member off the hook, or a mafia of engaging in extortion letting any particular member off-the-hook.
There are no effective, enforceable structures for stopping academic fraud. Most people who've engaged in it did not become persona non grata, let alone been fired; most continue to hold tenured academic jobs. Worst-case outcomes are a multiyear slap-on-the-wrist of some kind. That, combined with intense competition, led some people to cheat. Eventually, when it turned out to work okay, it became a culture of cheating.
Yes, the individuals involved should be punished, but much more important are:
1) Procedures for getting crooks out of the academy
2) Reducing competition, so there aren't the intense incentives to cheat
3) Implementing transparency. Why do universities get to use taxpayer dollars (grant / tuition overhead) and charitable contributions to cover up this stuff? Governance should be transparent and open. Data should be transparent and open whenever possible. NDAs and non-disparage agreements should be off-bounds.
4) Big money should be out. Yes, I know how much the typical professor gets paid, but the million-dollar salaries for presidents, Nobel laureates, and similar high-ranked positions distort things.
5) Related to the prior, conflict-of-interest provisions should be just a couple orders of magnitude more enforceable. The industry<->academia and especially startup<->academia pipelines help ground things and prevent things from getting detached, but if you're doing research to start a startup to make a few million dollars, that tends to apply extreme pressure to bake data.
6) Hiring and promotion structures shouldn't be so impact-focused. The easiest way to have impact is to tell politically-popular lies. P-hunt for data that shows liberals are smarter than conservatives, atheists are more open-minded than evangelicals, racism is wholesale, wokeness is the way to solve it, and so on. On this list, the really most harmful is when you reach correct conclusions from false data (much more so, even, than false conclusions from false data).
... and so on. This should happen wholesale, across academia, to be eligible for federal grants or tuition subsidies.