A method for controlling an industrial process , comprising: • determining , by a process controller , based at least in part on a set of current values and/or past values of state variables of the process , a set of control outputs to be applied to at least one actor and/or lower-level controller configured to cause the performing of at least one physical action on the process ; • querying , based on at least a subset of the set of current values and/or past values of state variables and on at least a subset of the set of control outputs, a trained machine-learning model that is configured to output a classification value , and/or a regression value , that is indicative of a propensity of a watching human operator to at least partially override the control outputs delivered by the process controller ; and • in response to determining that the classification value , and/or the regression value , and/or a propensity of the human operator overriding the control outputs that is computed from the classification value and/or from the regression value, meets a predetermined criterion , modifying at least one of the control outputs , and/or at least one parameter that characterizes the behavior of the process controller , and/or at least one constraint under which the process controller operates, with the goal of reducing the propensity , wherein this modifying comprises: causing the rate at which a state variable of the process increases or decreases to progressively slow, wherein this state variable is a state variable whose overshoot and/or undershoot is, according to the classification value and/or the regression value , a likely reason for the human operator to override the control output delivered by the process controller . • determining , by a process controller , based at least in part on a set of current values and/or past values of state variables of the process , a set of control outputs to be applied to at least one actor and/or lower-level controller configured to cause the performing of at least one physical action on the process ; • querying , based on at least a subset of the set of current values and/or past values of state variables and on at least a subset of the set of control outputs, a trained machine-learning model that is configured to output a classification value , and/or a regression value , that is indicative of a propensity of a watching human operator to at least partially override the control outputs delivered by the process controller ; and • in response to determining that the classification value , and/or the regression value , and/or a propensity of the human operator overriding the control outputs that is computed from the classification value and/or from the regression value, meets a predetermined criterion , modifying at least one of the control outputs , and/or at least one parameter that characterizes the behavior of the process controller , and/or at least one constraint under which the process controller operates, with the goal of reducing the propensity , wherein this modifying comprises: causing the rate at which a state variable of the process increases or decreases to progressively slow, wherein this state variable is a state variable whose overshoot and/or undershoot is, according to the classification value and/or the regression value , a likely reason for the human operator to override the control output delivered by the process controller . <EOS>