International Business Machines Corporation

Wenjing Liao

Peng Hui Jiang

Ze Ming Zhao

Xiao Tian Xu

Embodiments of the present disclosure provide systems and methods for implementing self-bias detection based on performance and importance. A disclosed computer implemented method aggregates continuous input data through a K-means clustering algorithm to reduce the number of aggregated sub-group data pairs, enabling a reduced calculation time for computing bias and enhanced performance. The self-bias detection identifies a scale factor and a balance factor of aggregated sub-group data pairs, which indicate the importance of the detected bias.

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Beijing, China

Wenjing Liao