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AI Ethics & Safety
15 Terms
A–Z Linked
AI Ethics & Safety
Terms related to fairness, transparency, privacy, safety, accountability, and responsible AI.
Select a term below or scroll through the full category.
Accountability
📘 Definition
Accountability means organisations and individuals remain responsible for the actions and outcomes of AI systems.
🪄 Simple Explanation
People remain responsible for AI.
🔑 Why It Matters
Accountability supports ethical deployment.
💡 Example
A company investigates an AI error and corrects it.
See Also
AI Ethics
📘 Definition
AI ethics is the study and application of moral principles that guide the responsible design, development, and use of artificial intelligence.
🪄 Simple Explanation
AI ethics helps people use AI responsibly.
🔑 Why It Matters
Ethical AI builds trust and reduces harm.
💡 Example
An organisation adopts ethical guidelines before deploying AI.
See Also
AI Safety
📘 Definition
AI safety focuses on preventing AI systems from causing unintended harm while ensuring they remain reliable and controllable.
🪄 Simple Explanation
AI safety aims to reduce harmful outcomes.
🔑 Why It Matters
Safe AI protects people and organisations.
💡 Example
Safety testing is completed before deploying an autonomous system.
See Also
Alignment
📘 Definition
Alignment is the goal of ensuring that AI systems consistently act according to intended human values and objectives.
🪄 Simple Explanation
Alignment keeps AI behaviour consistent with human goals.
🔑 Why It Matters
It is central to long-term AI safety research.
💡 Example
Researchers test whether an AI follows safety instructions under different conditions.
See Also
Bias Mitigation
📘 Definition
Bias mitigation refers to techniques used to identify, reduce, or eliminate unfair bias in AI systems.
🪄 Simple Explanation
Bias mitigation helps make AI fairer.
🔑 Why It Matters
It improves trust and equitable outcomes.
💡 Example
Developers rebalance training data to reduce discrimination.
See Also
Consent
📘 Definition
Consent is the informed permission given by individuals before their personal data is collected or used.
🪄 Simple Explanation
Consent gives people control over their data.
🔑 Why It Matters
It supports ethical and lawful AI practices.
💡 Example
A user agrees before an app analyses personal photographs.
See Also
Data Security
📘 Definition
Data security protects information from unauthorised access, loss, or misuse.
🪄 Simple Explanation
Secure data keeps AI systems trustworthy.
🔑 Why It Matters
Security is essential when handling sensitive information.
💡 Example
Customer records are encrypted before storage.
See Also
- Privacy
- Cybersecurity
- Responsible AI
Explainability
📘 Definition
Explainability is the ability to understand why an AI system produced a particular decision or prediction.
🪄 Simple Explanation
Explainability helps people understand AI decisions.
🔑 Why It Matters
It is especially important in high-stakes applications.
💡 Example
A doctor reviews why an AI recommended a diagnosis.
See Also
Fairness
📘 Definition
Fairness means AI systems should avoid unjust discrimination and produce equitable outcomes for different individuals or groups.
🪄 Simple Explanation
Fair AI treats people equitably.
🔑 Why It Matters
Fairness reduces harmful bias.
💡 Example
A hiring model is tested to ensure equal treatment across groups.
See Also
Governance
📘 Definition
AI governance consists of policies, standards, and oversight mechanisms that guide responsible AI use.
🪄 Simple Explanation
Governance provides rules for AI.
🔑 Why It Matters
Good governance reduces organisational risk.
💡 Example
A university creates an AI governance committee.
See Also
Human Oversight
📘 Definition
Human oversight ensures that people remain able to supervise, review, and intervene in AI decisions when necessary.
🪄 Simple Explanation
People remain involved in important AI decisions.
🔑 Why It Matters
Oversight reduces the impact of harmful mistakes.
💡 Example
A clinician reviews AI recommendations before treatment.
See Also
Privacy
📘 Definition
Privacy refers to protecting personal information collected, stored, and processed by AI systems.
🪄 Simple Explanation
Privacy safeguards personal data.
🔑 Why It Matters
Respecting privacy builds public trust.
💡 Example
Medical records are securely protected during AI training.
See Also
Responsible AI
📘 Definition
Responsible AI is the practice of designing and using AI systems that are fair, transparent, safe, accountable, and respectful of human rights.
🪄 Simple Explanation
Responsible AI focuses on trustworthy AI.
🔑 Why It Matters
It encourages good governance and public confidence.
💡 Example
A company reviews AI systems for fairness before release.
See Also
Risk Assessment
📘 Definition
Risk assessment identifies and evaluates potential harms that may arise from developing or deploying an AI system.
🪄 Simple Explanation
Risk assessment anticipates possible problems.
🔑 Why It Matters
It supports safer AI deployment.
💡 Example
An organisation evaluates the risks before launching an AI hiring tool.
See Also
Transparency
📘 Definition
Transparency refers to openness about how AI systems are built, used, and evaluated.
🪄 Simple Explanation
Transparency helps people understand AI.
🔑 Why It Matters
It supports accountability and trust.
💡 Example
Developers publish documentation describing an AI model.