Computing Bias Practice Test

Introduction: A school district considers using facial recognition to identify visitors at building entrances. Examples of Bias: Computing bias means a system produces unfair results for some people, often because it reflects human choices or unequal social conditions. In facial recognition, bias appears when the tool misidentifies people with darker skin tones or women more often than others, especially if the training photos include fewer examples from those groups. How Bias Emerges: Bias can emerge when a data set overrepresents one group, when labels include stereotypes, or when designers test the tool mostly on one population. Impacts: Misidentification can lead to students or parents being wrongly questioned, denied entry, or reported to security, which can increase stress and distrust. Mitigation Strategies: The district can test accuracy across demographic groups, use more representative data, add human review before action, and set clear limits on when the tool is used.

Based on the text, which strategy is used to mitigate computing bias?

Introduction: A state motor vehicle office uses facial recognition to detect identity fraud when people apply for driver’s licenses. Examples of Bias: Computing bias refers to unfair patterns in outcomes produced by a system, often linked to the data and assumptions behind it. Facial recognition bias can produce more false matches for certain demographic groups when the system is trained on images that do not represent the full population. How Bias Emerges: Bias can come from uneven data, earlier record-keeping practices, and limited testing across groups. Impacts: False matches can delay licenses, trigger investigations, or create lasting records that are hard to correct. Mitigation Strategies: The office can require human review, allow easy appeals, and publish regular reports on error rates by group.

Based on the text, which strategy is used to mitigate computing bias?