Module 4/5 25 min
Deploying AI responsibly
Step 1 / 3·The 4-principle framework
0/3 steps done
Learning objectives — after this module you will:
- Apply the 4-principle responsible-AI framework in TD
- Recognize bias risk in talent data
- Sketch a 3-step TD digital transformation roadmap
Learn ~8 min
The 4-principle framework
Have a standard set of criteria before approving any AI application.
4 principles of responsible AI in TD
| Principle | Meaning | Test question |
|---|---|---|
| TRANSPARENCY | Employees know which data is used, for what purpose | If you posted internally how the system works, would it cause outrage? |
| FAIRNESS | Check bias: AI trained on past data will repeat past biases | Do the suggested results skew systematically by gender, age, region, office/factory? |
| PRIVACY | Minimal data, minimal access, not used beyond the stated purpose | Who can see an individual's attrition-risk score? Do you need individual level, or is group level enough? |
| HUMAN ACCOUNTABILITY | Decisions affecting people always have an approver who can explain them | If an employee asks 'why was I rated this way' — who answers, and can they? |
Bias is not a theoretical risk
A famous lesson from the tech industry itself: an experimental AI CV-screening tool (widely reported in 2018) learned from past hiring data that skewed male — and taught itself to downgrade résumés with female signals. The project was scrapped. The lesson: past data contains past bias; not checking for bias is replicating unfairness at machine speed.
Key takeaway: 4 principles: transparency, fairness, privacy, human accountability — check BEFORE deploying.