Mitigating the Risks of AI

Introduction to AI for Work

James Chapman

AI Curriculum Manager, DataCamp

Mitigating Knowledge Fabrication

Trust but verify mindset

Always double-check critical information
- 📈 Numbers and data points
- 📖 Citations and references
- 🏛 Historical facts and dates
- 💻 Technical details

Mitigating Recency Ignorance

Always check the knowledge cutoff date
Verify anything that happened afterward
- 💰 Prices
- 🏛 Regulations
- 👷 Personnel changes
- 📰 News
🌐 Use web browsing support

Mitigating Biased Outputs

Review outputs critically
- 🤝 Hiring materials
- ✨ Marketing content
- 👉 Decision-making
❗ Bias-detection tools

Mitigating Sycophantic Outputs

🧭 Guide the AI towards objectivity and critical analysis
🤔 Remain skeptical of AI outputs
💬 Explicitly ask for critical feedback
- What are the potential problems with this approach?
- What are some alternative perspectives on this issue?

Mitigating Privacy and Data Exposure

Never share:
- 🔑 Passwords or credentials
- 🔍 Customer personal data (e.g., PII)
- 📣 Proprietary company information
- 📜 Confidential strategies or plans

Rule-of-thumb: if you wouldn't post it publicly, don't paste it into a standard AI tool

Your AI Safety Checklist

Your AI Safety Checklist

Your AI Safety Checklist

Your AI Safety Checklist

Your AI Safety Checklist

Your AI Safety Checklist

Let's practice!

Introduction to AI for Work

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