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Legal compliance alone is insufficient because a legal system can still be unfair, manipulative or culturally harmful."}]},{"id":"c41e3143-1b1d-4b67-9266-ee8305496122","cardType":"basic","points":2,"front":"Why can software designed for one culture harm another culture?","back":"Local values, customs and expectations may differ.","mcqOptions":[{"text":"Local values, customs and expectations may differ.","correct":true,"explanation":"A design based on one culture’s assumptions may conflict with another culture’s values, customs and expectations, causing offence, exclusion or other harm."},{"text":"Local hardware, networks and processors may differ.","correct":false,"explanation":"Different technology infrastructure can create technical problems, but this does not explain cultural harm. The relevant reason is that local values, customs and expectations may differ."},{"text":"Local algorithms, databases and interfaces may differ.","correct":false,"explanation":"Different software components may require redesign, but they are not the cultural reason for harm. Cultural harm can result when local values, customs and expectations differ."},{"text":"Local laws, passwords and file formats may differ.","correct":false,"explanation":"Legal differences may affect compliance, but passwords and file formats are technical concerns. The key cultural issue is that local values, customs and expectations may differ."}]},{"id":"a6a535cb-dbd7-4c18-a2e5-7fb2fb962df5","cardType":"cloze","points":null,"front":"A [ ] is any person or group affected by a system, or able to affect it.","back":"A stakeholder is any person or group affected by a system, or able to affect it.","mcqOptions":[{"text":"stakeholder","correct":true,"explanation":"A stakeholder is any individual or group affected by a system or able to influence it, such as users, employees, owners, regulators or the wider community."},{"text":"shareholder","correct":false,"explanation":"A shareholder owns part of a company through shares, but not every shareholder is directly affected by or able to influence a particular system. The correct term is stakeholder."},{"text":"administrator","correct":false,"explanation":"An administrator manages or controls a system, which is one possible stakeholder role. However, the wider definition of any affected or influential person or group is stakeholder."},{"text":"customer","correct":false,"explanation":"A customer uses or buys a product and may be a stakeholder, but stakeholders also include groups that are not customers. The broader correct term is stakeholder."}]},{"id":"c9662c4c-1120-49e7-bcc9-104865628674","cardType":"basic","points":3,"front":"List the three big digital capabilities highlighted in the specification.","back":"

Monitor behaviour.
Amass and analyse personal information.
Distribute, publish and disseminate personal information.

","mcqOptions":[{"text":"

Monitor behaviour.
Amass and analyse personal information.
Distribute, publish and disseminate personal information.

","correct":true,"explanation":"The specification highlights monitoring behaviour, collecting and analysing personal information, and distributing or publishing that information as major digital capabilities."},{"text":"

Monitor hardware.
Amass and analyse financial data.
Distribute, publish and disseminate software updates.

","correct":false,"explanation":"These capabilities replace behaviour and personal information with hardware, financial data and software updates. The specified capabilities concern monitoring behaviour and handling personal information."},{"text":"

Monitor networks.
Amass and analyse system logs.
Distribute, publish and disseminate source code.

","correct":false,"explanation":"Network monitoring and source-code publication are related computing activities, but they are not the three capabilities listed. The specification focuses on behaviour and personal information."},{"text":"

Monitor performance.
Amass and analyse transaction records.
Distribute, publish and disseminate technical documentation.

","correct":false,"explanation":"These are plausible digital activities, but they substitute system performance, transactions and documentation for the specified focus. The correct list concerns behaviour and personal information."}]},{"id":"7a6f3970-ec4f-479b-b5e9-28388867abcd","cardType":"cloze","points":null,"front":"To [ ] means to observe, record or track what people do.","back":"To monitor behaviour means to observe, record or track what people do.","mcqOptions":[{"text":"monitor behaviour","correct":true,"explanation":"To monitor behaviour means to observe, record or track what people do, such as their actions, movements, choices or online activity."},{"text":"predict behaviour","correct":false,"explanation":"Predicting behaviour means forecasting what people may do in the future, not observing or recording their actions. The correct term is monitor behaviour."},{"text":"analyse behaviour","correct":false,"explanation":"Analysing behaviour means examining recorded actions to identify patterns or conclusions. The act of observing, recording or tracking is called monitor behaviour."},{"text":"record behaviour","correct":false,"explanation":"Recording behaviour is one possible part of monitoring, but the definition includes observing, recording or tracking collectively. The complete term is monitor behaviour."}]},{"id":"f927a9c9-3450-4e0b-8c61-c775d635bacd","cardType":"basic","points":1,"front":"What monitoring risk means people may change behaviour because they know they are watched?","back":"Chilling effects.","mcqOptions":[{"text":"Chilling effects.","correct":true,"explanation":"Chilling effects occur when people alter or restrict their behaviour because they know their activity may be monitored, even without direct punishment or intervention."},{"text":"Self-censorship","correct":false,"explanation":"Self-censorship describes one possible response, but the monitoring risk itself is called chilling effects because awareness of surveillance changes behaviour more broadly."},{"text":"Privacy invasion","correct":false,"explanation":"Privacy invasion means personal information is accessed or exposed improperly; it does not specifically describe people changing behaviour because they know they are watched."},{"text":"Data breaches","correct":false,"explanation":"A data breach is unauthorised access to information, whereas the behavioural change caused by awareness of monitoring is known as chilling effects."}]},{"id":"bcf33425-054d-4afe-bff2-0912677dec71","cardType":"cloze","points":null,"front":"[ ] is data that identifies, or can help identify, a person; [ ] is data about other data.","back":"Personal information is data that identifies, or can help identify, a person; metadata is data about other data.","mcqOptions":[{"text":"Personal information is data that identifies, or can help identify, a person; metadata is data about other data.","correct":true,"explanation":"Personal information can identify a person, directly or indirectly, while metadata describes information about data, such as when, where or how it was created."},{"text":"Metadata is data that identifies, or can help identify, a person; personal information is data about other data.","correct":false,"explanation":"Metadata describes other data, so it cannot correctly fill the first definition. Personal information identifies a person, while metadata is data about other data."},{"text":"Anonymised data is data that identifies, or can help identify, a person; metadata is data about other data.","correct":false,"explanation":"Anonymised data is processed to remove identifying links, so it does not match the first definition. Metadata, rather than anonymised data, means data about other data."},{"text":"Personal information is data that identifies, or can help identify, a person; aggregated data is data about other data.","correct":false,"explanation":"Aggregated data combines information into summaries and is not the general term for data about other data. Personal information is the term for identifying data."}]},{"id":"83ed23ba-a4c5-4d85-b8f6-e07ade3ff1c8","cardType":"basic","points":2,"front":"Why can metadata reveal sensitive details even if it looks harmless?","back":"Patterns in metadata can reveal sensitive details.","mcqOptions":[{"text":"Patterns in metadata can reveal sensitive details.","correct":true,"explanation":"Metadata may appear harmless individually, but patterns in locations, times, contacts or activity can be combined to infer sensitive personal details."},{"text":"Contents in metadata can reveal sensitive details.","correct":false,"explanation":"The contents of a message may reveal sensitive details, but metadata concerns surrounding information such as timing and location; patterns in that metadata create the risk."},{"text":"Labels in metadata can reveal sensitive details.","correct":false,"explanation":"Labels can describe metadata, but they are not the reason it becomes revealing. Repeated patterns across metadata can expose habits, relationships or locations."},{"text":"Records in metadata can reveal sensitive details.","correct":false,"explanation":"Individual records may provide clues, but the key risk is that patterns across metadata reveal information that each apparently harmless item may conceal alone."}]},{"id":"da7de404-967d-4155-8069-72908f8782b3","cardType":"cloze","points":null,"front":"[ ] uses data to infer characteristics such as interests, income, health risks or likely behaviour.","back":"Profiling uses data to infer characteristics such as interests, income, health risks or likely behaviour.","mcqOptions":[{"text":"Profiling","correct":true,"explanation":"Profiling uses collected data to infer characteristics, preferences, risks or likely behaviour about individuals or groups, even when those characteristics were not explicitly provided."},{"text":"Classification","correct":false,"explanation":"Classification assigns data to categories, but profiling specifically uses data to infer characteristics such as interests, income, health risks or likely behaviour."},{"text":"Encryption","correct":false,"explanation":"Encryption transforms data to prevent unauthorised reading; it does not infer personal characteristics from data, which is the purpose of profiling."},{"text":"Anonymisation","correct":false,"explanation":"Anonymisation removes or obscures identifying links in data, whereas profiling analyses data to infer characteristics about people or groups."}]},{"id":"f42a346b-9712-4ba6-a154-12fc705617f8","cardType":"basic","points":1,"front":"What risk can fast digital publishing create when false claims spread faster than corrections?","back":"Misinformation.","mcqOptions":[{"text":"Misinformation.","correct":true,"explanation":"Misinformation is false or inaccurate information that spreads, often rapidly through digital publishing, regardless of whether the person sharing it intended to deceive others."},{"text":"Disinformation","correct":false,"explanation":"Disinformation is deliberately false information intended to deceive. The broader risk described here is misinformation, which includes false claims spread without that intention."},{"text":"Malinformation","correct":false,"explanation":"Malinformation is genuine information used maliciously, often by taking it out of context. False claims spreading faster than corrections describe misinformation."},{"text":"Propaganda","correct":false,"explanation":"Propaganda is biased or manipulative communication used to influence opinions, but the specific risk of false claims spreading is misinformation."}]},{"id":"1966d76b-98af-4cc9-9458-60e782d471d1","cardType":"cloze","points":null,"front":"Digital technology makes information easier to [ ] and easier to [ ].","back":"Digital technology makes information easier to collect and analyse and easier to copy and share.","mcqOptions":[{"text":"Digital technology makes information easier to collect and analyse and easier to copy and share.","correct":true,"explanation":"Digital technology supports large-scale data collection and analysis, while copying and sharing digital information can happen quickly and with little loss of quality."},{"text":"Digital technology makes information easier to store and ignore and easier to copy and share.","correct":false,"explanation":"Digital technology does make information easier to copy and share, but it makes information easier to collect and analyse, not store and ignore."},{"text":"Digital technology makes information easier to collect and analyse and easier to delete and hide.","correct":false,"explanation":"Digital systems can collect and analyse information, but deleting and hiding are not the general opportunities created by digital technology for information handling."},{"text":"Digital technology makes information easier to transmit and compress and easier to copy and share.","correct":false,"explanation":"Information can be transmitted digitally, but the key paired opportunities are collecting and analysing data and copying and sharing it efficiently."}]},{"id":"fb86baed-0d47-4662-8366-a95bfb8fe1d0","cardType":"basic","points":3,"front":"Why do software engineers have power over users and society?","back":"Their choices affect information seen, decisions made and behaviours encouraged.","mcqOptions":[{"text":"Their choices affect information seen, decisions made and behaviours encouraged.","correct":true,"explanation":"Software engineers influence what users encounter, how systems make or support decisions, and which behaviours platforms encourage through their design choices."},{"text":"Their choices affect hardware used, files stored and connections made.","correct":false,"explanation":"Software engineers can influence hardware and storage, but their wider social power comes from shaping information seen, decisions made and behaviours encouraged."},{"text":"Their choices affect data entered, programs run and devices connected.","correct":false,"explanation":"These are technical effects rather than the main social influence described. Software choices can shape information exposure, decisions and encouraged behaviour."},{"text":"Their choices affect passwords protected, errors detected and systems maintained.","correct":false,"explanation":"Security and performance matter, but they do not capture the ethical and social power of influencing what people see, decide and are encouraged to do."}]},{"id":"559ff3c9-f389-4c6f-9452-ee3c2ace0705","cardType":"cloze","points":null,"front":"Software is not neutral just because it is technical: algorithms and interfaces can embed [ ] and [ ] values.","back":"Software is not neutral just because it is technical: algorithms and interfaces can embed moral and cultural values.","mcqOptions":[{"text":"Software is not neutral just because it is technical: algorithms and interfaces can embed moral and cultural values.","correct":true,"explanation":"Algorithms and interfaces reflect choices about what is right or wrong and what matters to particular communities, so they can embed both moral and cultural values."},{"text":"Software is not neutral just because it is technical: algorithms and interfaces can embed economic and political values.","correct":false,"explanation":"Algorithms and interfaces can reflect economic and political priorities, but the paired values identified here are moral and cultural values."},{"text":"Software is not neutral just because it is technical: algorithms and interfaces can embed technical and financial values.","correct":false,"explanation":"Technical features describe implementation, and financial values concern money; neither pair captures the ethical and community-based values in the completed statement."},{"text":"Software is not neutral just because it is technical: algorithms and interfaces can embed legal and personal values.","correct":false,"explanation":"Software may involve legal and personal considerations, but the intended point is that design can encode judgements about morality and culture."}]},{"id":"d4ece23e-126b-435d-b644-94768a80382d","cardType":"basic","points":1,"front":"Which design choice embeds values by deciding whether privacy is opt-in or opt-out?","back":"Default settings.","mcqOptions":[{"text":"Default settings.","correct":true,"explanation":"Defaults shape behaviour because many users leave the initial choice unchanged. Making privacy opt-in or opt-out therefore embeds a value judgement about whether privacy or data collection is prioritised."},{"text":"Privacy policies.","correct":false,"explanation":"Privacy policies describe how data is handled, but they do not determine which choice is automatically selected. The opt-in or opt-out position is established by default settings."},{"text":"Encryption standards.","correct":false,"explanation":"Encryption standards protect data from unauthorised access rather than deciding whether users initially consent to data collection. That value judgement is embedded in the default settings."},{"text":"Accessibility testing.","correct":false,"explanation":"Accessibility testing checks whether people with different needs can use a system; it does not preselect privacy consent. Opt-in or opt-out behaviour is determined by default settings."}]},{"id":"45b83156-a345-48f8-89df-c0652b8ccd01","cardType":"basic","points":2,"front":"Why can biased past lending decisions make a loan-scoring algorithm unfair?","back":"It may learn and repeat those biased patterns.","mcqOptions":[{"text":"It may learn and repeat those biased patterns.","correct":true,"explanation":"A model trained on historical decisions can identify discriminatory patterns as if they were valid predictors, then reproduce them when assessing new applicants."},{"text":"It may detect and remove those biased patterns.","correct":false,"explanation":"Training does not automatically distinguish unfair bias from useful patterns. Without deliberate auditing and mitigation, the algorithm may learn and repeat the discrimination present in historical decisions."},{"text":"It may ignore and replace those biased patterns.","correct":false,"explanation":"An algorithm does not inherently discard biased examples or replace them with fair decisions. It may instead learn and repeat patterns represented in its training data."},{"text":"It may balance and correct those biased patterns.","correct":false,"explanation":"Using many historical cases does not guarantee that discrimination will average out. If biased decisions are systematic, the algorithm may learn and repeat those patterns."}]},{"id":"39bd26dd-1607-4262-a63f-13621ca33cc4","cardType":"basic","points":2,"front":"Why does deployment to millions increase a developer’s responsibility?","back":"A small design decision can become a large social consequence.","mcqOptions":[{"text":"A small design decision can become a large social consequence.","correct":true,"explanation":"When software reaches millions of people, even a minor choice can repeatedly affect behaviour, access or opportunities. Its accumulated social impact therefore greatly increases the developer’s responsibility."},{"text":"A small design decision affects only the original developer.","correct":false,"explanation":"Deployment at scale extends effects far beyond the development team. A small design decision can influence millions of users and become a large social consequence."},{"text":"A large user population makes each consequence less significant.","correct":false,"explanation":"A larger population multiplies rather than dilutes a system’s effects. Repeated across millions of users, a small design decision can become a large social consequence."},{"text":"A wider software release removes the need for ethical review.","correct":false,"explanation":"Wider deployment strengthens the need for ethical review because more people may be affected. A small design decision can scale into a large social consequence."}]},{"id":"ff1513b6-ab75-441e-82ef-ee2b09576ef4","cardType":"basic","points":2,"front":"Why do rapidly changing technologies make computing hard to legislate?","back":"New platforms, data uses and algorithms appear before laws catch up.","mcqOptions":[{"text":"New platforms, data uses and algorithms appear before laws catch up.","correct":true,"explanation":"Technology can develop and spread faster than legislation is debated and enacted. Existing laws may therefore fail to address the risks created by new platforms, data practices and algorithms."},{"text":"New platforms, data uses and algorithms appear after laws predict them.","correct":false,"explanation":"Legislators cannot reliably predict every future technological development. Computing is difficult to regulate because new platforms, data uses and algorithms often appear before laws catch up."},{"text":"New platforms, data uses and algorithms appear before users adopt them.","correct":false,"explanation":"Appearing before widespread adoption does not itself explain the legislative problem. The difficulty arises because technological developments can emerge and spread before laws catch up."},{"text":"New platforms, data uses and algorithms appear after courts regulate them.","correct":false,"explanation":"Courts and lawmakers generally respond after technologies create new disputes or harms. New platforms, data uses and algorithms often emerge before the relevant laws catch up."}]},{"id":"1e969277-c414-4c85-bb49-56745f99979a","cardType":"basic","points":2,"front":"Why do global digital services make legal enforcement difficult?","back":"Users, companies and data may be in different countries.","mcqOptions":[{"text":"Users, companies and data may be in different countries.","correct":true,"explanation":"Different countries have different laws and enforcement powers. When users, providers and stored data cross borders, it can be unclear which jurisdiction applies or can act effectively."},{"text":"Users, companies and data may be in the same country.","correct":false,"explanation":"Being in one country usually makes the applicable jurisdiction clearer. Enforcement becomes difficult when users, companies and data are spread across different countries with different laws."},{"text":"Users, companies and data may be on different devices.","correct":false,"explanation":"Using different devices creates technical issues but does not primarily cause international legal conflicts. Enforcement is difficult because users, companies and data may be located in different countries."},{"text":"Users, companies and data may use different passwords.","correct":false,"explanation":"Different passwords concern account security rather than legal jurisdiction. Global enforcement is difficult when users, companies and data are located in different countries governed by different laws."}]},{"id":"8657586e-a512-4b89-9b8f-e665e8d81e2d","cardType":"basic","points":2,"front":"What unintended consequence can a law requiring quick removal of harmful content cause?","back":"Over-removal of lawful speech.","mcqOptions":[{"text":"Over-removal of lawful speech.","correct":true,"explanation":"Platforms facing strict deadlines or penalties may remove borderline content without careful review. This encourages cautious moderation that also deletes lawful expression."},{"text":"Guaranteed protection of lawful speech.","correct":false,"explanation":"A rapid-removal duty does not guarantee protection for lawful expression. Fear of penalties may instead encourage platforms to remove borderline material, causing over-removal of lawful speech."},{"text":"Automatic promotion of harmful speech.","correct":false,"explanation":"The requirement concerns deleting content, not promoting it through recommendation systems. Its likely unintended consequence is over-removal of lawful speech when platforms act too cautiously."},{"text":"Permanent preservation of harmful speech.","correct":false,"explanation":"Quick-removal rules aim to reduce the availability of harmful content rather than preserve it. The unintended risk is over-removal of lawful speech through rushed or cautious moderation."}]}]}],"examCountdown":{"examDate":"2027-05-11","paperLabel":"Paper 1"},"heading":"Individual (moral), social (ethical), legal and cultural issues and opportunities","title":"Individual (moral), social (ethical), legal and cultural issues and opportunities Flashcards","topbar":{"backHref":"/as-level/computer-science/aqa/flashcards"},"topicSidebar":"$L39"}]}],"topicId":"85428706-a002-5dff-bd8d-3db1b8ec1b0b","walled":"$L3a"}],"$L3b","$L3c"]