Artificial Intelligence (AI) will transform how we work, shop, and solve problems in our daily lives. However, there could be serious mistakes if not properly identified early on with appropriate mitigations in place to eliminate serious mistakes.
Without appropriate AI guardrails, AI systems can inflict real harm on humans — by giving wrong answers or results, incorrect decisions that may sound too good but make absolute non-sense, or even getting stuck in loops (or infinite loops to drive you mad).
Below are common scenarios reflecting practical real-world examples:
1. Hallucinations: Making up non-sense
AI models can sometimes generate entirely false responses, results, or information. This is categorised as a hallucination. It's not a bug or glitch in the software, but a known limitation in the systems where words and patterns are not clearly defined in the "brain" of the AI model.
How to catch hallucinations:
- Cross-check AI responses against current business or organisational policies, data, or reports.
- Conduct regular reviews of policies for critical scenarios, especially finance and communications.
- Verify sources of information in alignment with the AI model used.
2. Loops: Going round in circles
There are situations where AI can fall into a state of endless loops — especially in chats/conversations or automated tasks. This can happen for a number of reasons: unclear or undefined requirements, rules, or conditions, or simply poor prompt design rendering the conversation into an infinite loop.
How to prevent continuous loops:
- Design clearly defined conditions and worst-case scenarios for conversations.
- Implement detection of loops (e.g., repeated statements, phrases, or intentions).
- Where the AI bot cannot close a conversation, hand off to a human customer service officer.
3. Bad decisions: No logic and unethical
If an AI agent is asked to make a decision based on logic and reasoning but provides one that isn't logical or is unethical, that poses real safety concerns — often due to poor training data or gaps in human intervention and handoff systems.
Checklist to avoid bad decisions:
- Use human checkpoints for high-risk decisions involving finance or health.
- Perform regular tests on decision models using real-world scenarios and edge cases.
- Continue to monitor false positive and false negative rates closely.
4. Over-generalisation: One size fits all
AI agents can over-generalise — applying general rules to specific cases where they can't appropriately reason or respond. This leads to decisions or messages that appear reasonable but are actually off-topic or don't make sense for the situation.
Checklist to reduce over-generalisation:
- Apply risk management to user/customer profiles with relevant profiling attributes.
- Continue training models on diverse, real-world data sets.
- Apply safety nets and guardrails for high-risk topics (health, legal, finance).
Conclusion
There are many scenarios in which AI needs guardrails — technical, ethical, logical, and procedural. Left unchecked without human intervention, these systems can make repetitive mistakes that cause reputational or personal damage.
For organisations, this means detailed checklists for developers, real-time monitoring tools, and human oversight for sensitive or high-risk cases. On a personal level, stay mindful of AI-driven responses, think critically, and validate outputs against authentic primary or secondary sources.