Erroneous Outcome Consequence Estimation Divergence

From The Foundation for Best Practices in Machine Learning
Technical Best Practices > Fairness & Non-Discrimination > Erroneous Outcome Consequence Estimation Divergence

Erroneous Outcome Consequence Estimation Divergence

Control

Document and assess the results of erroneous (false positive & false negative) outcome consequences, both real and perceived, specifically in terms of divergence between relevant (Sub)populations. If material divergence present, take measures to harmonise Outcome perceptions and/or mitigate erroneous Outcome consequences in Model design, exploration, development, and production.


Aim

To (a) ensure uniformity in erroneous Outcomes for (Sub)populations; (b) highlight outcome effects for different (Sub)populations; and (c) highlight associated risks that might occur in the Product Lifecycle.


Additional Information