Why A/B Testing AI Is Different From Traditional A/B Testing
Traditional A/B testing changes website UI or marketing copy. Users interact with variants and you measure business metrics. With AI, you change models or algorithms. The model's behavior depends on input data, so performance varies across different scenarios.
An AI model improving by 1 percent accuracy in the lab might decrease conversion 5 percent in production if the improvement helps wrong use cases. Proper A/B testing of AI systems requires careful design, statistical rigor, and focus on business metrics rather than just technical metrics.
Types of AI Experiments
Shadow Deployment
New model processes requests in parallel to production model but doesn't affect actual responses. Compare predictions from both to understand behavior differences. If new model makes obviously bad predictions, catch them before real users experience them.
Shadow deployment is low-risk but doesn't measure business impact (new model doesn't affect actual outcomes).
Canary Rollout
Route small percentage (5 to 10 percent) of traffic to new model. Monitor business metrics. If metrics are good, gradually increase percentage. If metrics degrade, rollback immediately.
Canary rollout measures real business impact but limits exposure if problems occur.
A/B Test
Random users see either old or new model. Compare business metrics between groups. Statistical tests determine if difference is significant.
A/B test measures impact but both models affect production simultaneously (requires careful monitoring).
Multi-Armed Bandit (MAB)
Continuously learn which model is better while still serving users. Route traffic to better-performing model more often. Dynamically adjust allocation as evidence accumulates.
MAB optimizes for user experience (serve best model more) while still testing alternatives. Faster learning than fixed A/B tests.
| Experiment Type | Risk Level | User Impact | Learning Speed |
|---|---|---|---|
| Shadow Deployment | Very Low | None | Slow |
| Canary Rollout | Low | Small | Medium |
| A/B Test | Medium | Balanced | Fast |
| Multi-Armed Bandit | Medium | Optimized | Very Fast |
Designing Rigorous AI Experiments
Before the Experiment: A/A Testing
Test with both groups seeing the same model. If metrics differ between groups for no reason, something is wrong (biased traffic splitting, temporal variation, etc.). Fix before running real experiment.
Define Your Hypothesis Clearly
"New model is better" is vague. "New model increases conversion rate from 5 percent to 5.5 percent with 95 percent confidence" is specific and testable.
Choose Primary and Secondary Metrics
Primary metric is what you care most about (revenue, conversion, satisfaction). Secondary metrics catch unintended consequences (if conversion increases but satisfaction decreases, something's wrong).
Calculate Required Sample Size
How many users do you need to see a meaningful difference? Larger differences need smaller sample sizes. Smaller improvements need larger samples. Statistical power calculators compute this.
Run Until Statistical Significance
Don't stop early just because results look good. Random variation can create false positives. Continue until reaching predetermined sample size or 95 percent confidence in the result.
Monitor for Unexpected Results
If primary metric improves 100x more than expected, something might be wrong (measurement bug, data issue). Investigate before celebrating.
Real-World Experimentation Example
Recommendation system team has current model (champion) achieving 2.5 percent click-through rate. New model (challenger) promises 3 percent in the lab. Design A/B test: 50,000 users per variant (100,000 total), track CTR as primary metric, engagement time as secondary metric.
Test runs for 1 week. Results: challenger achieves 3.1 percent CTR (statistically significant with 99 percent confidence). Engagement time unchanged. Confidence metrics show challenger is better. Rollout challenger to 100 percent of users gradually through canary.
After full rollout, continue monitoring. If metrics degrade unexpectedly, rollback to champion. Keep the new model only if metrics sustain over time.
Common Pitfalls in AI Experimentation
Multiple comparisons problem: if you test 20 variants, random chance alone will show some "winners" even if all are equivalent. Correct for multiple comparisons statistically.
Selection bias: if experiment traffic doesn't match overall traffic (unusual time of day, limited geography), results don't generalize.
Sybil attacks: in some contexts, users can create multiple accounts to vote for certain models. Detect and remove obvious manipulation.