Abstract: Systems and techniques for social responsibility load balancer are described herein. First user data for a first user and second user data for a second user is obtained. The first user data is aggregated into a first dataset and the second user data is aggregated into a second dataset. The first dataset and the second dataset are evaluated using a responsibility prediction learning model to determine a set of responsibilities. A target responsibility delta is calculated. The set of responsibilities are processed using a responsibility balancing algorithm to sort the set of responsibilities into the first responsibility assignments and the second responsibility assignments. A current delta is calculated between the first responsibility assignments and the second responsibility assignments. It is determined that the current delta is equal to the target responsibility delta. A user interface is generated to output the first responsibility assignments and the second responsibility assignments.