Final answer:
Prof. Blumenstock likely used supervised machine learning by training an algorithm with a labeled dataset to make predictions. Transparency in AI algorithms is crucial to mitigate biases, as discussed in the legal and ethical context by Professor Miriam Buiten.
Step-by-step explanation:
Prof. Blumenstock likely used supervised machine learning by providing a labeled dataset to the algorithm, where both the input features and the desired output are included. The algorithm then learns from this data to make predictions or decisions. A common example of supervised learning is a classifier, which might be used to determine the minimal set of tRNA identity positions for amino acids, as mentioned in the referenced study. Supervised learning typically involves the training phase, where the model is exposed to the input-output pairs, followed by the testing phase, where the trained model predicts outcomes based on new inputs.
In the context of legal and ethical considerations, as discussed by Professor Miriam Buiten, transparency in artificial intelligence algorithms is important to identify and address any potential biases that could arise during the machine learning process.