Machine learning is based on training and can be implemented through Reinforcement Learning. This technique is of clear skinnerian derivation: the actions that lead to the achievement of the objective are rewarded, while those that lead to defeat are punished, according to the sequential and rewarding logic that presides over the Task Analysis. While, however, in task analysis techniques carried out with children/students, personal or cultural parameters are taken into account, albeit superficially, this does not happen in Reinforcement Learning, where learning proceeds by trial and error and control and supervision remain the prerogative of the human being. The “choices” of machines, therefore, are conditioned by the programmer’s culture. One possibility is offered by Deep Reinforcement Learning, based on Convolutional Neural Networks that help the algorithm to plan, understand and strategically elaborate the actions to be taken, even if it remains risky, according to the authors, to delegate the interpretative and predictive aspect to the machines.
DE CASTRO, M., Zona, U., Bocci, F. (2020). L’apprendimento macchinico tra Skinner box e Deep Reinforcement Learning. Rischi e opportunità. In Dalle Teaching Machines al Machine Learning (pp. 29-35). Padova : Padova University Press.