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?
- Publish regular reports on error rates by group (correct answer)
- Treat every automated match as final and unchangeable
- Expand use to all government services without review
- Reduce staffing so appeals cannot be processed
Explanation: This question tests AP Computer Science Principles skills: understanding computing bias and its societal impact. Computing bias occurs when algorithms or data sets favor certain outcomes, often reflecting societal inequalities. In the passage, the impact of bias in motor vehicle office facial recognition is highlighted, showing how false matches can delay licenses or trigger investigations. Choice A is correct because it accurately reflects the passage's specific mitigation strategy to 'publish regular reports on error rates by group,' which helps identify and address bias patterns. Choice B is incorrect because it contradicts the passage's emphasis on human review and appeals. To help students: Emphasize identifying mitigation strategies that increase transparency and accountability. Watch for: Options that suggest removing oversight or making systems less reviewable.