Unawareness of sensitive characteristics is a common issue when constructing models, as these attributes may be omitted from the training data. However, due to correlations between protected and non-protected features, a model trained with unawareness of a protected characteristic can still lead to disparate impact or violate fairness requirements. It is thus imperative to address this oversight to create an equitable system.
Case Scenarios Where Unawareness (to a sensitive attribute) Can Be Used?
Unawareness (to a sensitive attribute) can
- Help reduce bias in recruitment and hiring by excluding sensitive attributes from the evaluation process. This ensures that candidates are considered solely based on their qualifications and skills.
- Be used to decrease bias within credit scoring by omitting sensitive characteristics from the assessment process. This guarantees that credit decisions are only based on creditworthiness and risk factors.
- Facilitate reducing prejudice in healthcare by excluding sensitive details from patient data analysis. This ensures that patients obtain appropriate care and treatment regardless of race, gender, or other sensitive traits.
- Contribute to diminishing bias in criminal justice by eliminating sensitive data from the assessment process. This paves the way for defendants to be evaluated fairly and only on the facts of their case, not influenced by factors such as race or gender.
- Lessen prejudice in advertising by leaving out sensitive information from the targeting system. This allows ads to be targeted according to consumer interests and behavior, uninfluenced by race or gender.
