AP Computer Science Principles Quiz: Computing Bias

What this quiz covers

This quiz focuses on Computing Bias, giving you a quick way to practice the rules, question types, and explanations that matter most for AP Computer Science Principles.

How to use this quiz

Try each quiz question before looking at the correct answer. Use the explanations to review missed ideas, then come back to similar questions until the pattern feels familiar.

Question 1

1. A facial recognition system is less accurate for women because it was trained mostly on pictures of men.
2. A college admissions algorithm is programmed to give extra weight to applicants who play a sport that is predominantly played by a single demographic group. (correct answer)
3. A voice recognition system struggles to understand speakers with a certain accent because they were not included in the audio training files.
4. A résumé-screening tool favors male candidates because it learned from a company's historical hiring records, which were heavily male.

Explanation: Correct. In this case, the programmer has made an explicit choice to prioritize a specific factor (a particular sport). This is a feature of the algorithm's logic or design. If that factor correlates with a demographic group, the algorithm itself is biased, regardless of the data it processes.

• A, C, and D are all examples of data bias, where the algorithm's biased output is a result of being trained on unrepresentative or historically biased data.

Question 2

1. The system will be highly accurate for everyone because the training dataset was very large.
2. The system will be less accurate in identifying individuals from demographic groups that were underrepresented in the training data. (correct answer)
3. The system will function correctly only if users have high-resolution cameras, regardless of their demographic group.
4. The system will pose a significant risk to data privacy because it stores images without user consent.

Explanation: Correct. The training data lacks diversity, which is a primary cause of data bias. The system will likely perform poorly for groups not well-represented in the training set, as it has not learned to identify their features effectively.

• A is incorrect because the size of a dataset does not guarantee fairness or prevent bias; the representativeness of the data is critical.
• C is incorrect because while camera quality can affect performance, the core issue of bias stems from the unrepresentative training data.
• D is incorrect because while data privacy is a valid concern for facial recognition, the question asks about the consequence of the biased training method, which relates to accuracy and fairness.

Question 3

1. The program will be unable to store images from patients of other ethnic backgrounds due to privacy restrictions.
2. The program will be highly accurate for all patients because it learned the fundamental features of the disease.
3. The program will run much more slowly when analyzing images of patients from different backgrounds.
4. The program's diagnostic accuracy will likely be lower for patients from ethnic backgrounds not included in the training data. (correct answer)

Explanation: Correct. The appearance of skin conditions can vary significantly across different skin tones. By training on data from only one group, the program has not learned to recognize these variations. This data bias will likely lead to reduced accuracy and potentially harmful misdiagnoses for underrepresented groups.

• A is incorrect. Privacy restrictions are a legal and data management issue, not a direct consequence of the biased training method.
• B is incorrect. The 'fundamental features' of a visual disease are often tied to how they appear on different skin types, so the model's learning is incomplete and biased.
• C is incorrect. The program's speed of execution is generally not affected by the content of the image in this way; the issue is with the accuracy of its analysis.

Question 4

1. The algorithm is too complex, making it difficult for bank employees to understand its decisions.
2. The historical data used to train the algorithm contained and reflected past societal biases. (correct answer)
3. Applicants from the specified geographic area are making more errors on their loan applications.
4. The bank's computer system lacks the necessary security features to protect applicant data.

Explanation: Correct. The algorithm learned patterns from data that was generated during a time of discriminatory practices. It is therefore perpetuating this historical bias by treating new applications in a similar way, even if the discriminatory policies are no longer in place. The data itself is the source of the bias.

• A is incorrect. Complexity and transparency are issues, but the root cause of the bias is the data.
• C is incorrect. This response blames the victims of the bias rather than identifying the flaw in the system.
• D is incorrect. Security is an important but separate concern from algorithmic fairness.

Question 5

1. The user interface of the tool makes it difficult for users to select a preferred gender for the output.
2. The program has a syntax error that causes it to default to male pronouns for certain professions.
3. The computer hardware running the translation lacks the processing power to consider gender-neutral options.
4. The algorithm was trained on a massive text dataset from the real world, which contains statistical biases about gender and professions. (correct answer)

Explanation: Correct. Language models learn from the patterns in the text they are trained on. If the training data (e.g., books, articles, websites) more frequently associates "doctor" with male pronouns and "nurse" with female pronouns, the model will learn and replicate this statistical correlation. This is a classic example of a computing innovation reflecting biases present in its training data.

• A is incorrect. The bias is in the core translation logic, not the user interface.
• B is incorrect. This is not a syntax error, but a logical outcome based on the model's training.
• C is incorrect. This is a software and data issue, not a hardware limitation.

Question 6

1. Symmetric key encryption
2. Algorithmic bias (correct answer)
3. Network fault tolerance
4. Crowdsourcing

Explanation: Correct. The algorithm is producing systematically different and unfair outcomes for different demographic groups (male vs. female users). This is a clear and direct example of algorithmic bias, likely learned from biased historical data about job applications or societal roles.

• A is incorrect. Symmetric key encryption is a cryptographic technique for securing data.
• C is incorrect. Fault tolerance is a principle of network design that ensures reliability.
• D is incorrect. Crowdsourcing is a method of gathering information or work from a large group of people.

Question 7

1. The program might violate the privacy of citizens by collecting data about their locations without their consent.
2. The program's predictions may not be 100% accurate, potentially causing police to be sent to areas where no crime occurs.
3. The historical arrest data may reflect biases in policing, leading the program to unfairly target certain neighborhoods and create a feedback loop. (correct answer)
4. The program's user interface could be difficult for police officers to use, leading to misinterpretation of the crime predictions.

Explanation: Correct. This highlights a critical societal impact of computing bias. If historical data reflects that certain neighborhoods were policed more heavily (regardless of actual crime rates), the algorithm will learn to identify these areas as high-risk. This can lead to increased police presence, more arrests, and a reinforcement of the original bias in future data.

• A is incorrect. Data privacy is a valid ethical concern, but the question is specifically about bias in the program's function.
• B is incorrect. Inaccuracy is a general problem for any predictive model, but the key ethical concern here is the systematic and unfair nature of the inaccuracy due to bias.
• D is incorrect. User interface design is a usability issue, not the core ethical problem of systemic bias.

Question 8

1. Random errors in a program's code that cause it to produce unpredictable results for all users.
2. Mistakes in the syntax of a programming language that prevent a program from running.
3. Systematic and repeatable errors in a computer system that create unfair outcomes for certain groups of people. (correct answer)
4. Security flaws in an algorithm that allow unauthorized users to gain access to sensitive information.

Explanation: Correct. This definition captures the key elements of algorithmic bias: it is systematic (not random), repeatable, and results in unfairness or prejudice against specific subgroups.

• A describes a buggy or unreliable program, not a biased one.
• B describes a syntax error, which is a coding mistake that a compiler or interpreter would catch.
• D describes a security vulnerability, which is distinct from the concept of fairness in a system's outcomes.

Question 9

1. If many users have searched for stereotypical or biased phrases, the algorithm will learn to suggest those phrases to new users. (correct answer)
2. The feature may be inaccurate and suggest completions that do not match what the user intended to type.
3. The feature stores a history of user searches, which raises concerns about data privacy and surveillance.
4. The feature requires a fast internet connection to work properly, which may disadvantage some users.

Explanation: Correct. The system learns from user behavior. If past user queries reflect societal biases (e.g., searching "CEOs are..." and getting completions that are gender-biased), the autocomplete feature will present these biases to new users as neutral suggestions, thereby amplifying and reinforcing the stereotypes.

• B is incorrect. Inaccuracy is a performance issue, but perpetuating stereotypes is a specific problem of bias.
• C is incorrect. Data privacy is a valid but separate concern from the content of the suggestions.
• D is incorrect. This relates to the digital divide, not the biased nature of the algorithm's output.

Question 10

1. A larger dataset always makes an algorithm less accurate before it becomes more accurate.
2. Collecting more data is expensive and may exceed the project's budget without solving the issue.
3. A larger dataset requires more storage space, which can make the computing innovation less scalable.
4. If the method of data collection is still flawed, the new, larger dataset will likely contain the same underlying bias. (correct answer)

Explanation: Correct. According to EK DAT-2.C.5, bias is not eliminated by simply collecting more data. If the source or method of collection is biased, a larger sample will just amplify that original bias. For bias to be reduced, the data must be more representative, not just more plentiful.

• A is incorrect. This is not a generally true statement about machine learning; more data typically improves accuracy if the data is of good quality.
• B is incorrect. While budget is a practical concern, it doesn't explain why the approach is fundamentally flawed from a technical perspective.
• C is incorrect. Storage and scalability are system design concerns, not the reason why the bias itself would persist.

Question 11

1. It can cause some students to be marked absent incorrectly (correct answer)
2. It ensures attendance records are always error-free
3. It improves lecture audio quality for all students
4. It affects only students who choose online classes

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 university attendance systems is highlighted, showing how facial recognition can have higher error rates for women and students with darker skin tones. Choice A is correct because it accurately reflects the passage's statement that 'Students may be marked absent incorrectly, which can affect grades and increase conflict with instructors.' Choice B is incorrect because it contradicts the passage's discussion of errors and bias. To help students: Emphasize identifying specific consequences mentioned in the text rather than idealized outcomes. Watch for: Answer choices that promise perfection or error-free performance when the passage discusses bias and errors.

Question 12

1. Require staff confirmation when the system flags a mismatch (correct answer)
2. Eliminate all alternative check-in methods immediately
3. Assume errors are random and ignore group differences
4. Use the tool to make treatment decisions by itself

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 hospital facial recognition systems is highlighted, showing how errors can delay care or create billing problems. Choice A is correct because it accurately reflects the passage's specific mitigation strategy to 'require staff confirmation for any mismatch.' Choice B is incorrect because the passage actually recommends keeping alternative check-in methods, not eliminating them. To help students: Focus on identifying specific mitigation strategies explicitly mentioned in the text. Watch for: Options that contradict the passage's recommendations or suggest removing safeguards rather than adding them.

Question 13

1. They are misidentified at higher rates than others (correct answer)
2. They receive automatic upgrades to premium seating
3. They are unaffected because the system is always neutral
4. They experience bias only in online ticket pricing

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 stadium facial recognition is highlighted, showing how the system can disadvantage certain demographic groups. Choice A is correct because it accurately reflects the passage's statement that 'Facial recognition bias can lead to higher misidentification rates for Black attendees and women when the training images underrepresent them.' Choice C is incorrect because it contradicts the passage's entire discussion of how bias creates uneven outcomes. To help students: Emphasize careful reading to identify which groups are specifically mentioned as experiencing higher error rates. Watch for: Options that claim systems are neutral when the passage explicitly discusses bias and uneven outcomes.

Question 14

1. Rely on a single vendor’s accuracy claims
2. Remove all human judgment from decisions
3. Test performance across different demographic groups (correct answer)
4. Use facial recognition for every school interaction

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 facial recognition at school entrances is highlighted, showing how it can misidentify people with darker skin tones or women more often due to underrepresentation in training data. Choice C is correct because it accurately reflects the passage's explanation of testing performance across different demographic groups as a specific mitigation strategy. Choice A is incorrect because relying on vendor claims without independent verification would not address bias issues. To help students: Encourage careful reading to identify specific mitigation strategies mentioned in passages, and discuss how testing across groups helps reveal hidden biases. Watch for: Options that sound plausible but aren't actually mentioned as mitigation strategies in the given text.

Question 15

1. Higher trust in police because technology seems neutral
2. Improved photo quality in security cameras across cities
3. Stressful stops or wrongful questioning after misidentification (correct answer)
4. Equal error rates for all demographic groups in practice

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 facial recognition is highlighted, showing how it leads to misidentification with serious personal consequences. Choice C is correct because it accurately reflects the passage's explanation of bias consequences including stressful police stops and wrongful questioning after misidentification. Choice A is incorrect because the passage actually states people lose trust in public safety agencies, not gain it. To help students: Encourage careful reading to identify specific consequences mentioned in the text, and discuss how technological errors can have real-world impacts on individuals. Watch for: Answer choices that present opposite effects or benefits when the passage describes harms.

Question 16

1. It produces more false matches for them than for white men (correct answer)
2. It guarantees perfect identification because the system is automated
3. It affects only camera hardware, not identification outcomes
4. It improves their match rates by reducing human review

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 facial recognition on Black women is highlighted, showing how it produces more false matches for them compared to white men. Choice A is correct because it accurately reflects the passage's specific example that the system flags more false matches for Black women than for white men. Choice B is incorrect because it contradicts the passage's point about unequal error rates and the existence of false matches. To help students: Encourage students to identify specific examples in passages that illustrate bias patterns, and discuss how underrepresentation in training data affects different groups unequally. Watch for: Answer choices that claim perfect accuracy or equal treatment when the passage explicitly describes disparate impacts.

Question 17

1. Publish regular reports on error rates by group (correct answer)
2. Treat every automated match as final and unchangeable
3. Expand use to all government services without review
4. 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.

Question 18

1. Training photos overrepresent some groups and underrepresent others (correct answer)
2. Bias comes only from broken cameras in older neighborhoods
3. Bias disappears when the system runs faster on new computers
4. Bias results mainly from people refusing to take any photos

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 emergence of bias in facial recognition is highlighted, showing how it stems from unrepresentative training data. Choice A is correct because it accurately reflects the passage's explanation that bias emerges when training photos include many light-skinned faces and fewer dark-skinned faces, creating overrepresentation and underrepresentation. Choice B is incorrect because it attributes bias to hardware issues rather than data representation problems. To help students: Encourage understanding of how training data composition directly affects algorithm performance, and discuss the importance of representative datasets. Watch for: Answer choices that blame hardware or speed when the passage focuses on data representation issues.

Question 19

1. It removes the need for any human judgment in investigations
2. It shifts errors toward groups underrepresented in the photo data (correct answer)
3. It makes all matches equally accurate across demographic groups
4. It mainly changes camera placement, not identification results

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 facial recognition is highlighted, showing how it shifts errors toward groups underrepresented in training data. Choice B is correct because it accurately reflects the passage's explanation that when training photos underrepresent certain groups, the system learns patterns that work better for overrepresented groups, shifting errors to underrepresented ones. Choice C is incorrect because it contradicts the passage's central point about unequal accuracy across demographic groups. To help students: Encourage analysis of how training data composition affects algorithm performance, and discuss the connection between data representation and outcome fairness. Watch for: Answer choices that claim equal outcomes when the passage explicitly describes unequal impacts.

Question 20

1. They are misidentified more often during boarding checks (correct answer)
2. They always pass screening faster than other travelers
3. They receive better flight discounts from airlines
4. They are affected only by baggage-handling delays

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 airport facial recognition is highlighted, showing how some systems misidentify travelers with darker skin tones at higher rates. Choice A is correct because it accurately reflects the passage's statement that these travelers experience higher misidentification rates during boarding checks, which can cause missed flights and extra screening. Choice B is incorrect because it contradicts the passage by suggesting these travelers have advantages rather than disadvantages. To help students: Emphasize careful reading to identify specific groups affected by bias and how they're impacted. Watch for: Answer choices that reverse or misrepresent the actual effects of bias described in the passage.