Active PAR

P7003

Algorithmic Bias Considerations

This standard describes specific methodologies to help users certify how they worked to address and eliminate issues of negative bias in the creation of their algorithms, where "negative bias" infers the usage of overly subjective or uniformed data sets or information known to be inconsistent with legislation concerning certain protected characteristics (such as race, gender, sexuality, etc); or with instances of bias against groups not necessarily protected explicitly by legislation, but otherwise diminishing stakeholder or user well being and for which there are good reasons to be considered inappropriate. Possible elements include (but are not limited to): benchmarking procedures and 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)

Sponsor Committee
C/S2ESC - Software & Systems Engineering Standards Committee
Learn More
Status
Active PAR
PAR Approval
2017-02-17

Working Group Details

Society
IEEE Computer Society
Learn More
Sponsor Committee
C/S2ESC - Software & Systems Engineering Standards Committee
Learn More
Working Group
ALGB-WG - Algorithmic Bias Working Group
Learn More
IEEE Program Manager
Christy Bahn
Contact
Working Group Chair
Ansgar Koene
No Active Projects
No Active Standards
No Superseded Standards
No Inactive-Withdrawn Standards
No Inactive-Reserved Standards
Newswire

Sign up for our monthly newsletter to learn about new developments, including resources, insights and more.