# Responsible Machine Learning — Machine Learning Basics

Source: https://www.skillbyai.com/en/machine-learning/p-responsible

> Models affect people; check how.

## Fairness, transparency and monitoring

Models learn from historical data, including its biases. Before deploying a model that affects people (loans, hiring, healthcare, pricing), check performance **per group** (for example error rates by region, age band or gender where lawful and appropriate), question whether features act as proxies for protected attributes, document the intended use and limits, keep a human in the loop for high-stakes decisions, and comply with applicable law. After deployment, **monitor**: input data drifts, the world changes, and accuracy decays, so plan retraining and re-evaluation.

## A model card outline

Document every model that affects people.

```text
model: loan-default-v3          owner: risk-ml team
intended use: rank applications for human review (not automatic rejection)
data: 2022-2025 applications, region coverage noted, known gaps listed
metrics: overall recall@precision 0.8; per-group error rates reported
limitations: not validated for business loans; drifts with interest rates
monitoring: monthly drift report; retrain quarterly or on alert
```

## Compare error rates by group

An average that looks fine can hide a much higher error rate for one group of people.

**Quiz:** Why monitor a model after deployment?

- [ ] Monitoring trains the model
- [ ] Models improve automatically
- [x] Data and the world change, so performance can decay
- [ ] It is only needed for regression

*Answer:* Data and the world change, so performance can decay. Deployment is the start, not the end.