The lesson here isn't "Human Judgement > Algorithmic Judgement". It also isn't "Humans are good at some things and computers are good at others". It's that there isn't a good reason to make the investment in solving this particular problem algorithmically (yet).
Algorithms are great when you need scale, especially in situations where a 10% improvement in prediction accuracy can make a big improvement in the bottom line. Netflix and other studios might greenlight several new shows in a year, out of dozens that receive consideration. And the Pareto Distribution is in full effect. Most of the profits and awards come from one or two big hits. Algorithmic decision making just doesn't make a lot of sense in situations with a small sample size and uneven reward structure.
It doesn't mean that it isn't possible, though. If someone were to make the massive investment necessary to do a more thorough analysis of the content creators, the actors, the scripts and potential audiences and all of the other possible inputs then algorithms could probably do as good a job as humans, if not better. Netflix and others have only taken baby steps in this direction, working with data that is readily available and using predictive techniques that are well tested and understood. Given the nature of the problem, it doesn't make sense for them to approach it any other way at this time. But when it comes to making billions of recommendations to millions of people per day, they still rely heavily on data and algorithmic prediction. There's a time and place for everything. The time and place for algorithms in our daily lives is changing and expanding, but very slowly.
Algorithms are great when you need scale, especially in situations where a 10% improvement in prediction accuracy can make a big improvement in the bottom line. Netflix and other studios might greenlight several new shows in a year, out of dozens that receive consideration. And the Pareto Distribution is in full effect. Most of the profits and awards come from one or two big hits. Algorithmic decision making just doesn't make a lot of sense in situations with a small sample size and uneven reward structure.
It doesn't mean that it isn't possible, though. If someone were to make the massive investment necessary to do a more thorough analysis of the content creators, the actors, the scripts and potential audiences and all of the other possible inputs then algorithms could probably do as good a job as humans, if not better. Netflix and others have only taken baby steps in this direction, working with data that is readily available and using predictive techniques that are well tested and understood. Given the nature of the problem, it doesn't make sense for them to approach it any other way at this time. But when it comes to making billions of recommendations to millions of people per day, they still rely heavily on data and algorithmic prediction. There's a time and place for everything. The time and place for algorithms in our daily lives is changing and expanding, but very slowly.