AI Ethics

A fluent or confident AI response is not proof that the information is correct, complete, or suitable for a particular person. Health decisions can have serious consequences, so the student should verify the information with a qualified professional or reliable medical source before acting. Option A follows safe, critical use of AI.

Spreading misleading information is not responsible because AI-generated or AI-amplified false claims can confuse people, damage reputations, and influence decisions unfairly. Translation, image recognition, and personalised recommendations can be responsible applications when they are used lawfully, explained appropriately, and checked for accuracy, privacy, and bias. A recommendation should not be treated as an unquestionable fact, but the application itself is not automatically irresponsible. Therefore, B is the clear answer.

AI systems learn from patterns in their training data. If that data overrepresents some groups or situations and underrepresents others, the model may produce systematically unfair results for the less-represented cases. Diverse data and regular evaluation can reduce this risk, while a clearly defined objective helps the project but cannot alone remove bias.

Diverse and balanced data gives the system examples from different relevant groups and situations, making underrepresentation less likely. It does not guarantee perfect fairness by itself, so outputs should also be tested and monitored. Using only one group’s data, hiding errors, or stopping evaluation can conceal or increase unfair performance.

Privacy concerns how personal information is collected, used, stored, shared, and protected, and whether people have appropriate control or permission regarding it. Preventing unauthorised access is an important security measure that supports privacy, but it is only one part of the broader idea. Processing speed and training time are performance issues, not privacy itself.

A sound privacy practice is to protect personal information, limit access, and share it only with appropriate permission and purpose. Passwords should never be posted publicly, and sensitive account details should not be entered carelessly on an unsecured network. Sharing without consent or exposing credentials can lead to misuse, identity theft, or unauthorised access.

Responsible AI use requires checking outputs for errors, limitations, or bias and protecting personal information throughout the process. Human judgement remains important, especially for significant decisions. Sharing an unchecked result, publishing private data, or removing all oversight can cause harm and make accountability difficult, so those actions do not represent responsible use.

If equally qualified candidates receive systematically lower scores because they belong to a particular group, the system is producing an unfair group-based disparity. That is a clear example of bias. Requesting documents for incomplete applications, sorting valid dates, or asking for a password change can be neutral procedures when applied consistently and for a relevant purpose.

AI-generated misinformation can look convincing even when it is false. People may trust it and make harmful decisions about health, money, safety, or social issues. AI does not automatically verify every claim before sharing, so users must check evidence and reliable sources rather than assuming that generated content is factual.

A loan decision can significantly affect a person, so an employee should review the AI recommendation, check the data, and look for unfair treatment or special circumstances before approving it. Restarting a computer, typing, and alphabetising files are routine actions and do not represent oversight of a high-impact AI decision.

AI ethics concerns how systems are designed and used so that they are safe, fair, responsible, and respectful of people. It includes privacy, consent, accountability, transparency, and reducing harmful bias. A machine’s physical size or the colour of a laboratory wall is not the central ethical issue in AI.

A responsible user treats AI output as assistance rather than unquestionable truth. The user should understand the result, check important facts, consider possible bias, protect private information, and use the output appropriately. Accepting every answer, inserting misleading data, or sharing private details violates responsible use.

In AI, bias means a systematic and unfair tendency in a system’s decisions or predictions that disadvantages, or sometimes unfairly favours, a person or group. It may arise from unbalanced training data, biased labels, model design, or the way the system is used. Therefore, option A correctly describes bias. Fast operation concerns speed, internet connection concerns networking, and image recognition concerns an AI capability, not fairness.

When designed and used responsibly, AI can assist healthcare workers, help identify useful information, and adapt learning material to a student’s needs. These benefits still require safety checks and human judgment. Misinformation, theft of personal information, and increased algorithmic bias are harmful social effects, not positive impacts.

AI systems learn from data, so inaccurate, incomplete, or biased training data can produce unreliable or unfair decisions. This is an important risk, especially when decisions affect people. Personalised practice, time savings, and translation can be useful applications, although they too should be checked and used responsibly.

People are more likely to trust an AI system when they can understand its purpose, know how it is used, and check whether its outputs are reasonable. Transparency and verification also support accountability when errors occur. Hiding mistakes, concealing the purpose, or sharing private information weakens safety and trust.

A chatbot may give information that is outdated, incomplete, or incorrect, even when the answer sounds confident. An examination date is important, so the student should compare it with the school’s official timetable or notice. Forwarding an unchecked answer could spread an error to other students.

Permission expresses informed consent and respects a person’s control over personal data, images, or voice recordings. It helps protect privacy and dignity, especially when the information may identify or affect someone. Faster processing, model accuracy, and storage capacity are technical matters and do not replace the need for consent.

AI outputs can be inaccurate, incomplete, outdated, or biased because the system may have limitations in its data, design, or interpretation. Checking important results helps a person identify problems, compare reliable evidence, and correct or reject the output. Human judgment remains important, especially for consequential decisions.

Historical selection data may reflect unequal opportunities or earlier unfair decisions. If the AI learns only from those patterns, it may reproduce or even strengthen the same bias when ranking new applicants. Training data alone does not guarantee fairness, so data review, fairness checks, and human oversight remain necessary.