What are Black Swans in AI?

According to the idea of data science, the only data they have is historical data, which is extremely poor

The nonexistence of black swans was a well-known fact for a very long time. Black swans weren't in any data because they had never been seen. If you had placed a wager at that time against the possibility that the following swan you saw would be black, you probably would have lost. It came out that black swans were numerous. Simply put, nobody had yet noticed them. In other words, the data did not reflect the real world, only the known and observable ones.

Additionally, that is a great opportunity to emphasize that the data is simply historical. And according to the idea of data science, the only data they have is historical data, which is extremely poor.

Individual Black Swans are extremely rare. The most recent Covid pandemic is evidence of a rare Black Swan occurrence.

One might also bring up the 2007 financial collapse. Since the housing market has never historically fallen, no model based on previous data could have anticipated such an occurrence.

On a collective scale, though, black swans are not uncommon. They occur more frequently than you might imagine. Pandemics, collapsed housing markets, or war in Europe seem like such infrequent occurrences. However, less noteworthy firsts and uncommon occurrences occur frequently.

So, be prepared for unexpected things like black swan events. Whatever your data from past events indicate, it is only history. It should only be relied upon with the understanding that things could drastically change in the future. That also extends to the accuracy of models. AI models factor in the past while calculating their accuracy as they employ a portion of historical training data. As a result, you should always anticipate that AI models will perform at least marginally worse in actual use than they do in testing.

Also, avoid attempting to forecast black swan events. They are by nature unpredictable. Rather, verify the procedures. A black swan could materialize at any time; therefore, decision models and levels, in particular, take this into account.