AP CSP Day 14: Bias In Data And Algorithms
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Algorithmic bias occurs when a model or decision system produces systematically unfair outcomes, often because the training data underrepresents certain groups. Data bias can stem from who collected the data, what was measured, or how samples were selected. AP CSP exam questions about bias ask students to identify potential sources of unfairness in a described algorithm or dataset. Recognizing that bias can be unintentional and that fairness requires deliberate effort in data collection and algorithm design is a key exam concept.
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Bias in Data and Algorithms
What Is Algorithmic Bias?
Algorithmic bias occurs when a computational system produces outcomes that are systematically unfair to certain groups. Bias often enters through training data that overrepresents some groups and underrepresents others.
Real-World Examples
A facial recognition system trained mostly on lighter-skinned faces performs poorly on darker-skinned faces. A resume screening algorithm trained on historical hiring data perpetuates past hiring discrimination. Neither programmer intended harm, but the data encoded existing inequities.
Practice Question
A hiring algorithm is trained on data from a company that historically hired mostly men for engineering roles. Which of the following outcomes is most likely?
Machine learning algorithms identify patterns in training data. If the historical data shows that men were hired more frequently, the algorithm learns to associate male-related features with successful candidates. This reproduces and potentially amplifies the existing bias.
A) Algorithms trained on biased data inherit that bias — they are not inherently objective. C) The algorithm considers all correlations in the data, including indirect demographic signals like names or activities. B) Algorithms do not self-correct for bias without explicit human intervention and redesign.
Students assume algorithms are inherently neutral because they are computer programs. In reality, algorithms reflect the biases present in their training data.
Biased training data leads to biased algorithms. On the AP exam, if a question mentions historical data with known disparities, the algorithm will likely perpetuate those disparities.
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