Learn the key differences between privileged and unprivileged groups and how AI systems can perpetuate unfair outcomes if bias is not addressed. Discover best practices for ensuring AI fairness.
Table of Contents
Question
Complete the sentence. A _______________ group traditionally receives more favorable outcomes compared to a _______________ group that traditionally receives less to no favorable outcomes.
A. privileged, unprotected
B. privileged, unprivileged
C. unprivileged, privileged
D. protected, privileged
Answer
B. privileged, unprivileged
Explanation
Privileged groups traditionally receive more favorable outcomes, while unprivileged groups traditionally receive less to no favorable outcomes.
In the context of AI fairness and bias, a privileged group refers to a segment of the population that has historically and systematically received favorable treatment, opportunities, and outcomes compared to other groups. Privileged groups typically have advantages due to factors like race, gender, socioeconomic status, etc.
In contrast, an unprivileged group is a historically disadvantaged or marginalized segment of the population that has received less favorable treatment and outcomes. Examples of unprivileged groups in many contexts include racial and ethnic minorities, women, LGBTQ+ individuals, people with disabilities, and those of lower socioeconomic status.
If training data reflects historical biases and unfair treatment towards unprivileged groups, AI systems trained on that data risk perpetuating and even amplifying those same biases and unfair outcomes. For example, an AI system for approving loans could end up unfairly rejecting qualified applicants from unprivileged groups at higher rates if the training data reflects historical lending discrimination.
To prevent AI systems from perpetuating unfair treatment of unprivileged groups, it’s critical to use techniques to identify and mitigate bias, such as:
- Using representative training data that reflects diversity
- Pre-processing data to remove bias
- Incorporating fairness metrics and constraints into model training
- Testing for fairness and disparate impact across sensitive attributes
In summary, privileged groups receive systematically favorable outcomes compared to unprivileged groups that are disadvantaged. AI systems must proactively address this to avoid perpetuating unfair treatment of already marginalized populations. The correct answer that completes the sentence is that privileged groups traditionally receive more favorable outcomes compared to unprivileged groups.
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