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An algorithm is a well-defined procedure that is used to solve a specific problem. In the context of artificial intelligence, algorithms are used to develop artificial intelligence systems that can perform specific cognitive tasks.

The correctness of an artificial intelligence algorithm refers to its ability to accurately and reliably solve the problem it was designed to solve. An artificial intelligence algorithm is considered correct if it consistently provides accurate and reliable results, regardless of the input information.

The validity of the application hypotheses of an artificial intelligence algorithm, on the other hand, refers to its ability to be used in real-world contexts and to provide valid results in those situations. To be considered valid, an artificial intelligence algorithm must be able to accurately and reliably solve the problem it was designed to solve, even when it is used in situations different from those for which it was originally designed. Application hypotheses are considered valid if they are realistic and relevant to the problem at hand.

For example, let’s imagine having an artificial intelligence algorithm that has been trained to predict the mortality risk of patients with a particular disease. If the algorithm has been trained using a representative sample of patients with this disease and if the model generated by the algorithm is able to accurately predict the mortality risk of the training sample patients, we can say that the algorithm is correct.

However, to evaluate the validity of the algorithm, we must test it on a new sample of patients that has not been used for training. If the algorithm can generalize its learning and accurately predict the mortality risk in this new sample of patients as well, we can say that the algorithm is valid.

In this case, it is important to note that the validity of the algorithm depends not only on its ability to generalize learning, but also on the representativeness of the training sample used. If the training sample is not representative of the general population, the algorithm may not be valid for predicting the mortality risk of other patients.

Another example we can bring from the context of autonomous vehicle driving on the road. If the algorithm has been trained using a representative sample of roads and driving situations, and if the model generated by the algorithm can correctly handle the vehicle in the situations included in the training data, we can say that the algorithm is correct.

However, to evaluate the validity of the algorithm, we must test it in situations that have not been included in the training. For example, we must verify if the algorithm can correctly handle the vehicle in case of adverse weather conditions (such as rain or snow), animals crossing the road or other unexpected situations. If the algorithm can generalize its learning and correctly handle the vehicle in these situations as well, we can say that it is valid.

As in the previous example, it is important to note that the validity of the algorithm also depends on the representativeness of the training sample used. If the training sample does not include a sufficient variety of situations and roads, the algorithm may not be valid for autonomous driving in other conditions.

It is therefore important to consider both the correctness and the validity of an artificial intelligence algorithm, as both are important to ensure that the algorithm functions reliably and is used appropriately.

Without correctness there is no solution, without validity there is no application.