Governance

AI Ethics

What it is, why it matters for businesses, and key questions to ask.

What it is

AI ethics covers bias, transparency, accountability, and fairness in how AI systems are designed, deployed, and used. It's about ensuring AI serves people fairly and doesn't perpetuate or amplify harm.

Why it matters for businesses

Biased training data can lead to biased outputs: discriminatory hiring tools, unfair lending, or skewed recommendations. Businesses are increasingly accountable to customers, regulators, and employees for how they use AI. Getting it wrong damages trust and can create legal risk.

Example workframe

Best practice

Define clear accountability: who owns AI decisions and outcomes?
Test for bias before deployment: use diverse test sets and edge cases
Document how the AI works in plain language for affected users
Human-in-the-loop for high-stakes decisions: AI assists, human decides
Review AI outputs periodically for drift or fairness issues

Areas to explore

Training data: where did it come from? Could it under-represent certain groups?
Output patterns: do results vary by demographic, region, or customer segment?
Explainability: can you explain why the AI gave a particular answer?
Redress: what happens if someone is harmed by an AI decision?
Procurement: do you have ethics criteria for AI vendor selection?

Suggestions

Create an AI ethics checklist for new use cases
Assign an ethics champion or committee for AI governance
Pilot with limited scope and monitor before scaling

Key questions to ask

Could our AI output discriminate against protected groups?
Can we explain how the AI reached its conclusion?
Who is accountable if the AI makes a wrong decision?
Have we tested for bias in our use case?
Do we have governance for AI procurement and deployment?