This standard describes processes and methodologies to help users address issues of bias in the creation of algorithms. Elements include: criteria for the selection of validation data sets for bias quality control; guidelines on establishing and communicating the application boundaries for which the algorithm has been designed and validated to guard against unintended consequences arising from out-of-bound application of algorithms; suggestions for user expectation management to mitigate bias due to incorrect interpretation of systems outputs by users (e.g. correlation vs. causation).
This standard is designed to provide individuals or organizations creating algorithms, largely in regards to autonomous or intelligent systems, processes and methodologies to provide clearly articulated accountability and clarity around how algorithms are targeting, assessing and influencing the users and stakeholders of said algorithm. This standard supports algorithm creators to communicate to users, and regulatory authorities, that best practices were used in the design, testing and evaluation of the algorithm to avoid unjustified differential impact on users.
New IEEE Standard – Active – Draft. This draft standard describes processes and methodologies to help users address issues of bias in the creation of algorithms. Possible elements include (but are not limited to): criteria for the selection of validation data sets for bias quality control; guidelines on establishing and communicating the application boundaries for which the algorithm has been designed and validated to guard against unintended consequences arising from out-of-bound application of algorithms; suggestions for user expectation management to help mitigate bias due to incorrect interpretation of systems outputs by users (e.g. correlation vs. causation).
This standard is designed to provide individuals or organizations creating algorithms, largely in regards to autonomous or intelligent systems, processes and methodologies to provide clearly articulated accountability and clarity around how algorithms are targeting, assessing and influencing the users and stakeholders of said algorithm. This standard supports algorithm creators to communicate to users, and regulatory authorities, that best practices were used in the design, testing and evaluation of the algorithm to avoid unjustified differential impact on users.
New IEEE Standard – Active – Draft. This draft standard describes processes and methodologies to help users address issues of bias in the creation of algorithms. Possible elements include (but are not limited to): criteria for the selection of validation data sets for bias quality control; guidelines on establishing and communicating the application boundaries for which the algorithm has been designed and validated to guard against unintended consequences arising from out-of-bound application of algorithms; suggestions for user expectation management to help mitigate bias due to incorrect interpretation of systems outputs by users (e.g. correlation vs. causation).
