# US 11,443,236 B2 · Enhancing fairness in transfer learning for machine learning models with missing protected attributes in source or target domains > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,236 B2 - Document ID: US11443236B2 - Status: Granted - Kind: B2 - Application number: 16/692,974 - Filed: 2019-11-22 - Published: 2021-05-27 - Granted: 2022-09-13 - Earliest claimed: 2019-11-22 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Karthikeyan Natesan Ramamurthy; Amanda Coston; Dennis Wei; Kush Raj Varshney; Skyler Speakman; Zairah Mustahsan; Supriyo Chakraborty - Web page: https://patentgrep.com/patent/US11443236B2 ## CPC - G06N 20/00: Machine learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method of utilizing a computing device to correct source data used in machine learning includes receiving, by the computing device, first data. The computing device corrects the source data via an application of a covariate shift to the source data based upon the first data where the covariate shift re-weighs the source data. ## Claims ### Claim 1 A method of utilizing a computing device to correct source data used in machine learning, the method comprising: receiving, by a computing device, first data; providing, by the computing device, transfer learning processing in absence of one or more protected attributes using a covariate shift combined with re-weighing to reduce a difference in group-specific prevalences for the source data; wherein the source data is partially labeled data, the first data is target data that includes the one or more protected attributes, and the transfer learning processing uses a target-fair covariate shift: wherein the target-fair covariate shift uses weights that are chosen to minimize a linear combination of a fairness loss with a classification loss; and wherein the fairness loss is evaluated on the target data where the one or more protected attributes are available. ### Claim 2 (depends on 1) The method of claim 1, further comprising: training, by the computing device one or more machine learning models using the re-weighed source data. ### Claim 3 (depends on 1) The method of claim 1, wherein the source data is fully labeled data having the one or more protected attributes, the first data is target data, and the transfer learning processing uses a prevalence-constrained covariate shift. ### Claim 4 (depends on 3) The method of claim 3, wherein the prevalence-constrained covariate shift uses learned weights based on a difference as compared to covariate shift weights subject to constraints on weighted prevalences. ### Claim 5 A computer program product for correcting source data used in machine learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: receive, by the processor, first data; provide, by the processor, transfer learning processing in absence of one or more protected attributes using a covariate shift combined with re-weighing to reduce a difference in group-specific prevalences for the source data; wherein the source data is partially labeled data, the first data is target data that includes the one or more protected attributes, and the transfer learning processing uses a target-fair covariate shift; wherein the target-fair covariate shift uses weights that are chosen to minimize a linear combination of a fairness loss with a classification loss; and wherein the fairness loss is evaluated on the target data where the one or more protected attributes are available. ### Claim 6 (depends on 5) The computer program product of claim 5, wherein the program instructions executable by the processor further causes the processor to: train, by the processor, one or more machine learning models using the re-weighed source data. ### Claim 7 (depends on 5) The computer program product of claim 5, wherein the source data is fully labeled data having the one or more protected attributes, the first data is target data, and the transfer learning processing uses a prevalence-constrained covariate shift. ### Claim 8 (depends on 7) The computer program product of claim 7, wherein the prevalence-constrained covariate shift uses learned weights that are chosen based on a difference as compared to covariate shift weights subject to constraints on weighted prevalences. ### Claim 9 An apparatus comprising: a memory configured to store instructions; and a processor configured to execute the instructions to: receive first data; provide transfer learning processing in absence of one or more protected attributes using a covariate shift combined with re-weighing to reduce a difference in group-specific prevalences for a source data; wherein the source data is partially labeled data, the first data is target data that includes the one or more protected attributes, and the transfer learning processing uses a target-fair covariate shift; and wherein the target-fair covariate shift uses weights that are chosen to minimize a linear combination of a fairness loss with a classification loss, and the fairness loss is evaluated on the target data where the one or more protected attributes are available. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein the processor is configured to further execute the instructions to: train one or more machine learning models using the re-weighed source data. ### Claim 11 (depends on 9) The apparatus of claim 9, wherein the source data is fully labeled data having the one or more protected attributes, the first data is target data, and the transfer learning processing uses a prevalence-constrained covariate shift. ### Claim 12 (depends on 11) The apparatus of claim 11, wherein the prevalence-constrained covariate shift uses learned weights that are chosen based on a difference as compared to covariate shift weights subject to constraints on weighted prevalences.