5 Steps to Audit Your Medical Device for California AI Bias

Bias in medical AI isn't just unethical—in California, it’s now a major litigation risk. 🩺

The Legal Landscape

California's Unruh Civil Rights Act and recent AI-specific legislation prohibit discrimination by algorithms. If your medical device performs worse for certain demographic groups, you could face lawsuits and regulatory action.

Step 1: Review Training Data Demographics

Start at the source. Analyze the demographic breakdown of your training data. Does it reflect the diversity of California's population? If your data is 90% from one demographic group, your model will likely underperform for others.

Step 2: Test Sub-Group Performance

Don't just look at overall accuracy (e.g., "99% accurate"). Break it down by race, gender, age, and socioeconomic status. A model can be 99% accurate overall but only 60% accurate for a specific minority group. This disparity is where liability lies.

Step 3: Analyze False Positives/Negatives

Look at the types of errors. Are false positives (e.g., false alarms) more common in one group? Are false negatives (e.g., missed diagnoses) more common in another? Missed diagnoses are particularly dangerous and litigious.

Step 4: Document Your Methodology

Show your work. Keep detailed records of your testing protocols, the datasets used, and the results. If you identify bias, document the steps you took to mitigate it. If you are sued, your audit trail is your primary defense.

Step 5: Continuous Monitoring

Bias can "drift" over time as patient populations change or as the model interacts with real-world data. Implement a system for continuous monitoring of model performance across demographic groups.