CoreQ Academy4.2 Explain risks associated with AI.
Responsible AI principles describe what good looks like; the risk list describes what goes wrong when they are ignored.
Responsible AI
- Fairness
- Outcomes do not unjustly disadvantage people or groups.
- Reliability and safety
- The system performs consistently and fails safely.
- Transparency
- People know when AI is used and how it broadly works.
- Privacy and security
- Personal data is protected and the system resists attack.
- Explainability
- Decisions can be explained in terms people understand, especially high-impact ones.
- Inclusiveness
- The system works for people with different abilities, languages and backgrounds.
- Accountability
- Named people are responsible for the system and its outcomes.
- Consistency
- Similar inputs produce similar, predictable outputs over time.
Risks
- Introduction of bias
- Skewed data or design produces unfair outcomes, creating legal and ethical harm.
- Accidental data leakage
- Staff paste confidential data into public AI tools, or models reveal training or retrieved data.
- Reputational loss
- Public AI failures, offensive outputs or misuse damage trust in the brand.
- Accuracy and performance of the model
- Hallucinations, drift and degraded performance lead to wrong decisions.
- Intellectual Property (IP)-related risks
- Training on content without rights, outputs that reproduce protected work, or loss of your own IP to AI providers.
- Autonomous systems
- AI that acts without human review can cause fast, large-scale harm when wrong or manipulated.
Check your understanding
Loading quiz…
Original CoreQ Academy material, mapped to objective 4.2 of the publicly available CompTIA SecAI+ CY0-001 exam objectives (Exam objectives V1, document version 1.1). Not affiliated with or endorsed by CompTIA. Contains no exam questions.