Abstract: Systems and methods may reduce bias in an artificial intelligence model. The system may receive word embedding model generated based on a corpus of words. The system may determine a bias definition vector in an embedding space of the word embedding model. The system may receive bias classification criteria. The bias classification criteria may include logic to group word vectors in the word embedding model based on a distance measurement from the bias definition vector. The system may identify, in the word embedding model, a first group of vectors and a second group of vectors based on the bias classification criteria and the bias definition vector. The system may generate a debiased artificial intelligence model. The debiased artificial intelligence model may include associations between words and metrics. The system may weight the metrics for the words associated with the first and second group of vectors with a non-zero penalization factor.

Abstract: Systems and methods may reduce bias in an artificial intelligence model. The system may receive word embedding model generated based on a corpus of words. The system may determine a bias definition vector in an embedding space of the word embedding model. The system may receive bias classification criteria. The bias classification criteria may include logic to group word vectors in the word embedding model based on a distance measurement from the bias definition vector. The system may identify, in the word embedding model, a first group of vectors and a second group of vectors based on the bias classification criteria and the bias definition vector. The system may generate a debiased artificial intelligence model. The debiased artificial intelligence model may include associations between words and metrics. The system may weight the metrics for the words associated with the first and second group of vectors with a non-zero penalization factor.

Abstract: The systems and methods to reduce bias in an artificial intelligence model are provided. The system may receive word embedding model generated based on a corpus of words. The system may determine a bias definition vector in an embedding space of the word embedding model. The system may receive bias classification criteria. The bias classification criteria may include logic to group word vectors in the word embedding model based on a distance measurement from the bias definition vector. The system may identify, in the word embedding model, a first group of vectors and a second group of vectors based on the bias classification criteria and the bias definition vector. The system may generate a debiased artificial intelligence model. The debiased artificial intelligence model may include associations between words and metrics. The system may weight the metrics for the words associated with the first group of vectors and second group of vectors with a non-zero penalization factor.

Abstract: A heuristic algorithm for solving a load balancing problem that carries out scheduling of plurality of tasks in two phases. In the first phase some tasks of the plurality of tasks are assigned to resources, they are best on, on a per resource basis and in the second phase resources are chosen for the remaining tasks of the plurality of tasks such that the length of the schedule is minimized.

Abstract: Load balancing is the problem of assigning tasks to a plurality of resources in a way such that the assignment is optimal in some sense. This problem has been of significant industrial importance where jobs, that require same or different amount of time on various machines, have to be assigned to machines, to balance the load, such that the maximum amount of time taken to complete all the tasks is minimized. An additional condition of this problem is that every job, mentioned above, is assigned to only one machine for its completion. Here I propose a heuristic algorithm for solving this load balancing problem that carries out scheduling in two phases. In the first phase some tasks are assigned to resources, they are best on, on a per resource basis and in the second phase resources are chosen for the remaining tasks such that the length of the schedule is minimized.