Artificial intelligence (AI) is changing our world. From chatbots to medical tools, AI systems touch our lives every day. But AI also raises big questions: How do we make sure it is fair? Who is responsible when something goes wrong? To answer these questions, we need to understand the language of AI ethics. This glossary will introduce 20 essential terms in clear, simple English. You will learn what each term means, why it matters, and see an example you can relate to.
1. Bias
Definition: When an AI system treats some people unfairly because of patterns in its training data.
Example: A hiring tool that rejects resumes from candidates in certain ZIP codes because past data showed fewer hires from those areas. This can unfairly block qualified applicants.
2. Fairness
Definition: The goal of making AI decisions equal and impartial for all groups.
Example: A lending platform checks that its loan approval rates are similar across different ethnic groups to avoid discrimination.
3. Transparency
Definition: Making AI processes and decisions open and understandable.
Example: A medical AI shows which symptoms and data points led to its diagnosis suggestion, so doctors can see why it reached that conclusion.
4. Accountability
Definition: Having clear responsibility for AI outcomes—good or bad.
Example: A company publicly states that its chief ethics officer will review all AI-related complaints and take action if needed.
5. Explainability
Definition: The ability to explain how an AI system arrived at a decision.
Example: A credit scoring AI uses a simple model that shows how much each factor (income, payment history) influenced the final score.
6. Privacy
Definition: Protecting personal data used or generated by AI systems.
Example: A health app anonymizes patient records so AI can learn from data without revealing anyone’s identity.
7. Security
Definition: Safeguarding AI systems from attacks or misuse.
Example: A chatbot uses encryption and authentication to prevent hackers from injecting harmful instructions.
8. Human-in-the-Loop
Definition: Keeping a person involved in AI decision-making to check and correct the AI.
Example: In content moderation, AI flags potentially harmful posts, but a human reviewer decides if they should be removed.
9. Informed Consent
Definition: Ensuring people know and agree to how their data is used by AI.
Example: A website shows a clear notice asking visitors for permission before collecting their browsing data for personalization.
10. Data Governance
Definition: Rules and processes for managing data quality, access, and use.
Example: A retail company has policies on who can view customer purchase histories and how long that data is kept.
11. Robustness
Definition: An AI system’s ability to handle unexpected situations without failing.
Example: A speech-recognition tool works even when there is background noise or an accent it did not see during training.
12. Reliability
Definition: Consistent performance of an AI system under normal conditions.
Example: A navigation app gives accurate directions every time you use it, day or night.
13. Scalability
Definition: The ability of an AI system to handle growing amounts of work or data.
Example: A photo-tagging service that can process millions of images per day without slowing down.
14. Ethical Design
Definition: Building AI systems with moral values and human well-being in mind.
Example: A social media algorithm is designed to promote content that fosters healthy discussions rather than sensationalism.
15. Algorithmic Auditing
Definition: Reviewing AI algorithms to check for bias, errors, or security issues.
Example: A third-party firm inspects a loan approval model to ensure it does not unfairly reject certain applicants.
16. Consent Management
Definition: Systems and tools to collect and manage user permissions for data use.
Example: A mobile game asks players to opt in before tracking their in-game behavior for analytics.
17. Differential Privacy
Definition: A technique that adds noise to data so individual entries cannot be identified.
Example: A public health database publishes statistics on disease rates without revealing any single patient’s record.
18. Model Drift
Definition: When an AI model’s performance degrades over time because data patterns change.
Example: A language model trained before a new slang term became popular may fail to understand recent tweets.
19. Red Teaming
Definition: Testing AI systems by simulating attacks or misuse scenarios.
Example: Security experts try to fool a facial recognition system by wearing disguises to check its vulnerability.
20. Societal Impact
Definition: The overall effect of AI systems on society, including economic, social, and cultural changes.
Example: An AI-driven automation tool may increase efficiency but also shift job markets, requiring new training programs for workers.
How to Use This Glossary
- For Beginners: Start by reading each term and example. Think about where you’ve seen these ideas in real life.
- For Teams: Share this glossary with your group to build a common language. Discuss how each concept applies to your projects.
- For Educators: Use these entries to teach students about AI ethics. Create quizzes or group activities around them.
Conclusion
AI ethics can seem complex, but knowing the key terms helps you join the conversation. This glossary covers the essentials—from bias and fairness to privacy and societal impact. Keep it handy as you read news, design AI tools, or discuss policies. When everyone speaks the same language, we can work together to build AI that benefits all.