Computing Bias - AP Computer Science Principles
Card 1 of 30
Identify a potential bias in loan approval algorithms.
Identify a potential bias in loan approval algorithms.
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Discrimination based on zip code. Location serves as proxy for protected characteristics.
Discrimination based on zip code. Location serves as proxy for protected characteristics.
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What is confirmation bias in computing?
What is confirmation bias in computing?
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Favoring information confirming preconceptions. Developers design systems based on their existing beliefs.
Favoring information confirming preconceptions. Developers design systems based on their existing beliefs.
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What is data bias?
What is data bias?
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Bias introduced by data selection or processing. Problems in how data is collected, labeled, or preprocessed.
Bias introduced by data selection or processing. Problems in how data is collected, labeled, or preprocessed.
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Which option is a consequence of biased algorithms?
Which option is a consequence of biased algorithms?
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Discrimination. Biased algorithms systematically disadvantage certain groups.
Discrimination. Biased algorithms systematically disadvantage certain groups.
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Identify a legal concern related to computing bias.
Identify a legal concern related to computing bias.
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Violation of anti-discrimination laws. Biased systems can violate equal protection laws.
Violation of anti-discrimination laws. Biased systems can violate equal protection laws.
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What is exclusion bias?
What is exclusion bias?
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Excluding relevant data points. Important subgroups are left out of the analysis.
Excluding relevant data points. Important subgroups are left out of the analysis.
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Identify a common cause of measurement bias.
Identify a common cause of measurement bias.
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Inaccurate data collection tools. Sensors or surveys that systematically misrepresent reality.
Inaccurate data collection tools. Sensors or surveys that systematically misrepresent reality.
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What is the role of transparency in reducing bias?
What is the role of transparency in reducing bias?
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Allows scrutiny and correction of biases. Open algorithms enable identification and correction of bias.
Allows scrutiny and correction of biases. Open algorithms enable identification and correction of bias.
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Which bias occurs when outcomes reinforce pre-existing biases?
Which bias occurs when outcomes reinforce pre-existing biases?
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Feedback bias. System outputs create conditions that reinforce the original bias.
Feedback bias. System outputs create conditions that reinforce the original bias.
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What is a proxy variable?
What is a proxy variable?
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Variable used as a substitute for another. Indirect measure that may introduce unintended correlations.
Variable used as a substitute for another. Indirect measure that may introduce unintended correlations.
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Identify a strategy to mitigate bias in AI systems.
Identify a strategy to mitigate bias in AI systems.
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Regular audits for bias detection. Systematic testing helps identify discriminatory patterns early.
Regular audits for bias detection. Systematic testing helps identify discriminatory patterns early.
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What is the impact of bias in automated decision-making?
What is the impact of bias in automated decision-making?
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Potential for unfair outcomes. Biased systems create systematic disadvantages for certain groups.
Potential for unfair outcomes. Biased systems create systematic disadvantages for certain groups.
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Identify a form of bias in predictive policing.
Identify a form of bias in predictive policing.
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Racial profiling. Using location as proxy for race creates discriminatory outcomes.
Racial profiling. Using location as proxy for race creates discriminatory outcomes.
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What role does diversity play in reducing bias?
What role does diversity play in reducing bias?
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Promotes varied perspectives and reduces bias. Different backgrounds challenge assumptions and identify blind spots.
Promotes varied perspectives and reduces bias. Different backgrounds challenge assumptions and identify blind spots.
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Identify a method to detect bias in datasets.
Identify a method to detect bias in datasets.
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Statistical analysis. Mathematical tests reveal unequal treatment across groups.
Statistical analysis. Mathematical tests reveal unequal treatment across groups.
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What is the significance of fairness in AI ethics?
What is the significance of fairness in AI ethics?
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Ensures equitable treatment across groups. All groups should receive equal quality of service.
Ensures equitable treatment across groups. All groups should receive equal quality of service.
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What is anchoring bias?
What is anchoring bias?
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Relying too heavily on initial information. Initial information disproportionately influences final decisions.
Relying too heavily on initial information. Initial information disproportionately influences final decisions.
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Identify the bias in historical data algorithms.
Identify the bias in historical data algorithms.
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Perpetuating outdated norms. Past discrimination gets encoded into present-day decisions.
Perpetuating outdated norms. Past discrimination gets encoded into present-day decisions.
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What is the role of accountability in computing bias?
What is the role of accountability in computing bias?
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Holds developers responsible for outcomes. Developers must answer for discriminatory algorithm outcomes.
Holds developers responsible for outcomes. Developers must answer for discriminatory algorithm outcomes.
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Which bias is due to overfitting a model?
Which bias is due to overfitting a model?
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Model bias. Models trained too specifically fail to generalize fairly.
Model bias. Models trained too specifically fail to generalize fairly.
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What is the impact of data imbalance on bias?
What is the impact of data imbalance on bias?
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Skews algorithm results. Unequal group representation distorts learned patterns.
Skews algorithm results. Unequal group representation distorts learned patterns.
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Which type of bias is caused by flawed assumptions?
Which type of bias is caused by flawed assumptions?
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Confirmation bias. Programmers' assumptions get embedded into the algorithm.
Confirmation bias. Programmers' assumptions get embedded into the algorithm.
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Which term describes bias from unrepresentative samples?
Which term describes bias from unrepresentative samples?
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Selection bias. Samples don't accurately represent the broader population.
Selection bias. Samples don't accurately represent the broader population.
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What is algorithmic bias?
What is algorithmic bias?
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Bias arising from algorithm design and implementation. Occurs when the algorithm's structure inherently favors certain groups.
Bias arising from algorithm design and implementation. Occurs when the algorithm's structure inherently favors certain groups.
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Identify one source of bias in computational data.
Identify one source of bias in computational data.
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Biased training data. Training datasets reflecting existing prejudices lead to biased models.
Biased training data. Training datasets reflecting existing prejudices lead to biased models.
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What is the definition of computing bias?
What is the definition of computing bias?
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Systematic errors in algorithms affecting outcomes unfairly. These errors create unequal treatment across different groups.
Systematic errors in algorithms affecting outcomes unfairly. These errors create unequal treatment across different groups.
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What role does diversity play in reducing bias?
What role does diversity play in reducing bias?
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Promotes varied perspectives and reduces bias. Different backgrounds challenge assumptions and identify blind spots.
Promotes varied perspectives and reduces bias. Different backgrounds challenge assumptions and identify blind spots.
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Which type of bias results from algorithm simplifications?
Which type of bias results from algorithm simplifications?
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Overgeneralization bias. Algorithms make broad assumptions that miss important nuances.
Overgeneralization bias. Algorithms make broad assumptions that miss important nuances.
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Identify a method to detect bias in datasets.
Identify a method to detect bias in datasets.
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Statistical analysis. Mathematical tests reveal unequal treatment across groups.
Statistical analysis. Mathematical tests reveal unequal treatment across groups.
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What is the impact of data imbalance on bias?
What is the impact of data imbalance on bias?
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Skews algorithm results. Unequal group representation distorts learned patterns.
Skews algorithm results. Unequal group representation distorts learned patterns.
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