I'm not entirely sure how helpful it would be to see these algorithms since many of them rely on machine learning models underneath the hood (IE: a model that p

I'm not entirely sure how helpful it would be to see these algorithms since many of them rely on machine learning models underneath the hood (IE: a model that predicts the likelihood a user will click a given video). Is it useful for the average person to know that some gradient boosted tree/deep learning model spat out a probability estimate? More information certainly does not hurt, but I doubt the average citizen/gov regulator can do much with this knowledge. These are not the kind of static bfs/dfs/quicksort/etc algorithms we learned in undergrad that can be dissected so easily. The ML models that underlie the recommendations/ranking "algorithms" are constantly changing based on the data they're trained on. Does this constitute an algorithm change? Disclaimer: this is just my understanding of how these newsfeed type algorithms work based on conversations with friends who work on these teams at FB/GOOG. Please do correct me if I got something wrong here.