I define fairness as consistent performance and respectful outcomes across different groups. I avoid deploying a system that works well for one subgroup but fails or harms another. 

> Data bias- the training data underrepresents certain groups or contexts.

> Model bias- the algorithm learns patterns that disadvantage subgroups.

> Deployment bias- the tool performs differently in real environments than in testing.

I continuously monitor fairness, not just once.  I...

1. Define metrics (accuracy, error rates, false positives/negatives).

2. Measure across subgroups.

3. Investigate gaps.

4. Adjust data, processes, and models.

5. Document outcomes.

“What I show users” section

• I state what the AI can and cannot do.
  
  

• I show confidence/limitations when relevant.
  
  

• I provide a human-help option and escalation pathway.

I keep humans in the loop for high-risk situations. I route safety concerns to appropriate support or clinical escalation pathways rather than letting the AI “handle it alone.”