Systems and methods used to enhance artificial intelligence systems by mitigating harmful artificial intelligence actions

A method of training an emotional response engine for use in an artificial intelligence (AI) system, comprising:

• determining a range of use of the AI control system;
• inputting a real-life scenario that may occur in the context of the AI control system into an emotional response engine;
• outputting a predicted emotional state set from the emotional response engine in response to the input of the real-life scenario into the emotional response engine;
• presenting the potential action outcome to each of a plurality of human subjects;
• detecting brain activity of the plurality of human subjects in response to presenting the potential action outcome to each of the plurality of human subjects;
• determining a plurality of emotional state sets respectively for the plurality of human subjects based on the detected brain activity of the plurality of human subjects;
• and
• updating the emotional response engine based on the predicted emotional state set and the plurality of determined emotional state sets.

An artificial intelligence (AI) control system, comprising:

• memory configured for storing an emotional response engine;
• at least one sensor configured for sensing an external environment of the artificial intelligence system;
• at least one processor is configured for generating a plurality of real-life scenarios based on the sensed external environment, inputting each of the plurality of real-life scenarios into the emotional response engine, such that the emotional response engine respectively outputs a plurality of predicted emotional state sets, inputting the plurality of predicted emotional state sets into a cost function or a reward function, such that the cost function or reward function outputs a plurality of scores respectively for the plurality of real-life scenarios, selecting one of the plurality of real-life scenarios based on the plurality of scores;
• and
• one or more actuators configured for performing an action associated with the selected real-life scenario.

A method of operating an artificial intelligence (AI) control system, comprising:

• sensing an external environment;
• generating a plurality of real-life scenarios based on the sensed external environment;
• inputting each of the plurality of real-life scenarios into an emotional response engine;
• outputting a plurality of predicted emotional state sets from the emotional response engine;
• inputting the plurality of predicted emotional state sets into a cost function or a reward function;
• outputting a plurality of scores from the cost function or reward function respectively for the plurality of real-life scenarios;
• selecting one of the plurality of real-life scenarios based on the plurality of scores;
• and
• performing an action associated with the selected real-life scenario.