Challenges facing algorithmic solutions

In addition to designing, developing, and testing an algorithm, in many cases, it is important to consider certain practical aspects of starting to rely on a machine to solve a real-world problem. For certain algorithms, we may need to consider ways to reliably incorporate new, important information that is expected to keep changing even after we have deployed our algorithm. For example, the unexpected disruption of global supply chains may negate some of the assumptions we used to train a model predicting the profit margins for a product. We need to carefully consider whether incorporating this new information will change the quality of our well-tested algorithm in any way. If so, how is our design going to handle it?

Expecting the unexpected

Most solutions to real-world problems developed using algorithms are based on some assumptions. These assumptions may unexpectedly change after the model has been deployed. Some algorithms use...