AI Ethics

One-sided or unrepresentative data may leave some groups, situations, or experiences missing. If an AI system learns from that incomplete picture, its predictions can systematically favour some cases and disadvantage others. Balanced data, clear goals, and human review can help detect or reduce bias, although they do not guarantee perfection.

Diverse and balanced data gives the system examples from different groups, conditions, and relevant situations. This can reduce the chance that the model will learn a narrow or unfair pattern. It does not guarantee fairness by itself; testing, monitoring, appropriate design, and human review are also important for finding remaining problems.

Privacy means handling personal information in a way that protects people from unauthorised access, disclosure, or misuse. Responsible practice includes collecting only what is needed, seeking appropriate consent, securing the data, and respecting its purpose and retention limits. Collecting without consent or sharing publicly can violate privacy.

A confident chatbot answer can still contain a factual error, so confidence is not evidence of correctness. The student should compare the date with an independent, reliable source such as a textbook, museum, or official archive. Asking the same chatbot again is not independent verification because it may repeat the same error.

AI safety aims to prevent or reduce harm that could result from incorrect, unsafe, misused, or unexpected system behaviour. Safety may involve testing, safeguards, monitoring, privacy protection, and human oversight. It does not mean blocking all data access, removing people from supervision, or merely making the interface look attractive.

Responsible AI considers how a system affects people throughout its design and use. It includes safety, fairness, privacy, transparency, accountability, appropriate human oversight, and checking for errors or harmful effects. Spreading misinformation, stealing personal data, and removing evaluation are irresponsible practices that can undermine trust and cause harm.

Transparency makes the working or reasoning of an AI system understandable to users. They can examine which information, rules, or patterns influenced a result. This supports trust, accountability, and correction of mistakes. It does not hide errors, automatically remove privacy, or delete data; those are separate issues.

AI systems can produce inaccurate, biased, or unsafe results because their data, design, or interpretation may be imperfect. Human supervision allows people to review outputs, notice risks, protect affected users, and correct decisions when needed. Keeping a machine switched off, sharing private information, or ignoring results is not the purpose of supervision.

An AI decision affecting opportunities, safety, or access to services is high-impact. An incorrect or biased result in such a case can cause serious real-world harm, so human review is especially important. Low-risk suggestions such as colours, titles, or note organisation may still be checked, but their likely consequences are usually smaller.

Misinformation can confuse people, damage reputations, influence decisions, and sometimes create safety risks. Using AI to produce or spread it is therefore improper and harmful, especially when the creator knows the information is false or fails to check it. Responsible use requires accuracy checks, honest presentation, and consideration of possible effects on others.

AI ethics concerns how AI should be designed and used so that it respects people and avoids unnecessary harm. Important concerns include fairness, privacy, safety, transparency, accountability, and responsible use. The colour of a machine, wall length, or paper thickness may be physical details, but they do not define the ethical quality of an AI system.

A responsible user treats AI output as assistance rather than unquestionable truth. The user should understand the purpose, check important facts and possible bias, protect privacy, and use the result ethically. Accepting every output, spreading misinformation, or sharing private information without consent can cause harm and violates responsible digital behaviour.

Good digital citizenship means using digital tools safely, respectfully, and responsibly while considering other people’s rights. It includes protecting privacy, checking information, obtaining consent, and communicating appropriately. Stealing data, spreading misinformation, or sharing someone’s images without permission violates privacy or trust, so option C is the only positive example.

Risk awareness helps people identify problems such as biased decisions, privacy violations, inaccurate predictions, security failures, or unsafe recommendations. Testing, monitoring, human review, and safeguards can then reduce the chance or impact of harm. Attractive appearance is unrelated, while rushed deployment and blind acceptance increase risk rather than controlling it.

AI is best learned through curiosity, questions, examples, and practice. Asking for clarification and connecting ideas with real situations builds conceptual understanding and helps a student evaluate results thoughtfully. Memorising without understanding is limited, accepting unchecked information is unreliable, and avoiding difficult problems prevents improvement. Thus, option B describes the strongest learning behaviour.

AI output may contain factual errors, missing information, unsuitable recommendations, or bias inherited from data and system design. Checking important results helps users compare them with evidence, notice unfair effects, and correct mistakes before acting. Verification does not make AI less useful; it makes its use safer and more reliable. AI can produce many kinds of outputs, not only numbers.

Responsible AI begins with a clear, beneficial, and safe purpose. It also requires suitable and reliable data, attention to privacy and fairness, testing for errors, and human oversight of important outputs. Accepting results blindly, using inaccurate or biased data, or ignoring privacy can harm people and therefore contradict responsible use.

The governing concept is digital ethics, especially honesty and academic integrity when using technology. Looking at another candidate’s screen gives an unfair source of answers and violates the examination rules, regardless of whether the device or network works properly. File storage, network speed, and screen brightness describe technical conditions, not the student’s dishonest conduct. Thus option D is the only relevant answer.