Google has turned into a cesspool. Half the time I find myself having to do ridiculous search contortions to get somewhat useful results - appending site: .edu or .gov to search strings, searching by time periods to eliminate new "articles" that have been SEOed to the hilt, or taking out yelp and other chronic abusers that hijack local business results.
Yes, they can. They should simply stop measuring only positives, and start measuring negatives - e.g. people that press the back button of their browser, or click the second, third, fourth result afterwards...which should hint the ML classifiers that the first result was total crap in the first place.
But I guess this is exactly what happens if you have a business model where leads to sites where you provide ads give you a weird ethics, as your company profits from those scammers more than from legit websites.
From an ML point of view google's search results are the perfect example of overfitting. Kinda ironic that they lead the data science research field and don't realize this in their own product, but teach this flaw everywhere.
Take a look sometime at the wealth of data google serp sends back about your interactions with it
Technically just open google serp in developer tools, network tab, set preserve/persist logs option, and watch the requests flowing back - all your clicks and back navigations are reported back for analysis. Same on other search engines. Only DDG doesn't collect your clicks/dwell time - but that's a distinguishing feature of their brand, they stripped themselves of this valuable data on purpose.
This touches the broader subject of systems engineering and especially validation. As far as I am aware, there are currently no tools/models for validation of machine learning models and the task gets exponentially harder with degrees of freedom given to the ML system. The more data Google collects and tries to use in ranking, the less bounded ranking task is and therefore less validatable, therefore more prone to errors.
Google is such a big player in search space that they can quantify/qualify behavior of their ranking system, publish that as SEO guidelines and have majority of good-faith actors behave in accordance, reinforcing the quality of the model - the more good-faith actors actively compete for the top spot, the more top results are of good-faith actors. However, as evidenced by the OP and other black hat SEO stories, the ranking system can be gamed and datums which should produce negative ranking score are either not weighted appropriately or in some cases contribute to positive score.
Google search results are notoriously plagued with Pinterest results, shop-looking sites which redirect to chinese marketplaces and similar. It looks like the only tool Google has to combat such actors is manual domain-based blacklisting, because, well, they would have done something systematic about it. It seems to me that the ranking algorithm at Google is given so many different inputs that it essentially lives its own life and changes are no longer proactive, but rather reactive, because Google does not have sufficient tools to monitor black hat SEO activity to punish sites accordingly.