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This deck focuses on Computing Bias, giving you a quick way to review the definitions, rules, and examples that matter most for AP Computer Science Principles.
Study Computing Bias in AP Computer Science Principles with focused flashcards that help you recognize the idea, recall the key rule, and apply it in practice-style prompts.
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Identify a method to detect bias in datasets.
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Statistical analysis. Mathematical tests reveal unequal treatment across groups.
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This deck focuses on Computing Bias, giving you a quick way to review the definitions, rules, and examples that matter most for AP Computer Science Principles.
Work through these flashcards in short sessions. Try to answer each prompt before flipping the card, then revisit any cards you miss until the explanation feels automatic.
Answer: Statistical analysis. Mathematical tests reveal unequal treatment across groups.
Answer: Holds developers responsible for outcomes. Developers must answer for discriminatory algorithm outcomes.
Answer: Overreliance on automated systems. Humans trust computer decisions without proper scrutiny.
Answer: Relying too heavily on initial information. Initial information disproportionately influences final decisions.
Answer: Allows scrutiny and correction of biases. Open algorithms enable identification and correction of bias.
Answer: Bias arising from algorithm design and implementation. Occurs when the algorithm's structure inherently favors certain groups.
Answer: Unconscious attitudes affecting decisions. Hidden prejudices influence seemingly objective decisions.
Answer: Sampling bias. Occurs when data collection methods favor certain groups.
Answer: Unfair treatment of individuals or groups. Algorithms perpetuate discrimination against protected classes.
Answer: Promotes varied perspectives and reduces bias. Different backgrounds challenge assumptions and identify blind spots.
Answer: Bias arising from algorithm design and implementation. Occurs when the algorithm's structure inherently favors certain groups.
Answer: Favoring information confirming preconceptions. Developers design systems based on their existing beliefs.
Answer: Inaccurate data collection tools. Sensors or surveys that systematically misrepresent reality.
Answer: Perpetuating outdated norms. Past discrimination gets encoded into present-day decisions.
Answer: Holds developers responsible for outcomes. Developers must answer for discriminatory algorithm outcomes.
Answer: Ensures equitable treatment across groups. All groups should receive equal quality of service.
Answer: Unfair treatment of individuals or groups. Algorithms perpetuate discrimination against protected classes.
Answer: Systematic errors in algorithms affecting outcomes unfairly. These errors create unequal treatment across different groups.
Answer: Underrepresentation of certain groups in data. Some demographics are missing or undersampled in training data.
Answer: Confirmation bias. Programmers' assumptions get embedded into the algorithm.
Answer: Violation of anti-discrimination laws. Biased systems can violate equal protection laws.
Answer: Selection bias. Samples don't accurately represent the broader population.
Answer: Skews algorithm results. Unequal group representation distorts learned patterns.
Answer: Racial profiling. Using location as proxy for race creates discriminatory outcomes.
Answer: Biased outcomes reinforce biased data. Algorithm decisions create new biased data for future training.
Answer: Allows scrutiny and correction of biases. Open algorithms enable identification and correction of bias.
Answer: Statistical analysis. Mathematical tests reveal unequal treatment across groups.
Answer: Confirmation bias. Programmers' assumptions get embedded into the algorithm.
Answer: Discrimination based on zip code. Location serves as proxy for protected characteristics.
Answer: Bias introduced by data selection or processing. Problems in how data is collected, labeled, or preprocessed.
Answer: Favoring information confirming preconceptions. Developers design systems based on their existing beliefs.
Answer: Overgeneralization bias. Algorithms make broad assumptions that miss important nuances.
Answer: Trend bias. Algorithm learns patterns that may not apply universally.
Answer: Underrepresentation of certain groups in data. Some demographics are missing or undersampled in training data.
Answer: Model bias. Models trained too specifically fail to generalize fairly.
Answer: Potential for unfair outcomes. Biased systems create systematic disadvantages for certain groups.
Answer: Use diverse and representative datasets. Inclusive datasets prevent algorithms from learning harmful patterns.
Answer: Skews algorithm results. Unequal group representation distorts learned patterns.
Answer: Excluding relevant data points. Important subgroups are left out of the analysis.
Answer: Bias introduced by data selection or processing. Problems in how data is collected, labeled, or preprocessed.
Answer: Promotes varied perspectives and reduces bias. Different backgrounds challenge assumptions and identify blind spots.
Answer: Discrimination. Biased algorithms systematically disadvantage certain groups.
Answer: Variable used as a substitute for another. Indirect measure that may introduce unintended correlations.
Answer: Regular audits for bias detection. Systematic testing helps identify discriminatory patterns early.
Answer: Overgeneralization bias. Algorithms make broad assumptions that miss important nuances.
Answer: Discrimination. Biased algorithms systematically disadvantage certain groups.
Answer: Ensures equitable treatment across groups. All groups should receive equal quality of service.
Answer: Discrimination based on zip code. Location serves as proxy for protected characteristics.
Answer: Feedback bias. System outputs create conditions that reinforce the original bias.
Answer: Excluding relevant data points. Important subgroups are left out of the analysis.
Answer: Systematic errors in algorithms affecting outcomes unfairly. These errors create unequal treatment across different groups.
Answer: Inaccurate data collection tools. Sensors or surveys that systematically misrepresent reality.
Answer: Biased training data. Training datasets reflecting existing prejudices lead to biased models.