It's fair to some extent. The human brain is vastly more complex, so I don't love the comparison. However, with very modern machine learning which is based on deep neural networks, even though the source code is simple, it produces a very complex neural network after training on data. That network is very hard to understand and predict.
Even with simpler algorithms, you might argue that in a complex environment they could have some emergent behavior that's hard to foresee based on the source code. For example, if one is not careful then even simple linear regression on some datasets can "learn" techniques like redlining -- using zip code as a proxy for race as a part of biased predictions.
Even with simpler algorithms, you might argue that in a complex environment they could have some emergent behavior that's hard to foresee based on the source code. For example, if one is not careful then even simple linear regression on some datasets can "learn" techniques like redlining -- using zip code as a proxy for race as a part of biased predictions.