A computing device (e.g., a mobile phone, camera, tablet computer, etc.) uses a contextual bandit machine-learning model (e.g., an artificial intelligence model) for reverting accidental user inputs. The computing device may execute an application that employs a user interface to facilitate human-machine interaction. The computing device may collect contextual user data and use a probabilistic model (e.g., a contextual bandit machine learning module) to analyze the collected contextual data to determine a confidence interval for specific user input. The application may receive an indication of the user input, e.g., tapping a particular icon within a graphical user interface (GUI) of the computing device to send to another user. The user may accidentally tap the wrong icon and unknowingly send an unintended communication to the other user. The contextual bandit, may, upon receiving and analyzing context data, determine, within a confidence interval, that the user likely did not intend to send the tapped icon. In this case, the computing device may display a message confirming the user’s intention to send the selected icon. The contextual bandit updates the probabilistic model based on the user’s response to improve the model’s future decisions of whether to query the user about suspected incorrect user inputs.
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Carbune, Victor and Damian, Alexandru, "REVERTING ACCIDENTAL KEY TAPS USING CONTEXTUAL BANDITS", Technical Disclosure Commons, (July 23, 2019)