cs249r_book/mlsysim/docs/api/solvers.ResponsibleEngineeringModel.qmd

# solvers.ResponsibleEngineeringModel { #mlsysim.solvers.ResponsibleEngineeringModel }

```python
solvers.ResponsibleEngineeringModel()
```

Models the computational cost of responsible AI practices (Wall 20: Safety).

This model quantifies the 'Safety Tax' — the additional compute and data
required for differential privacy or fairness guarantees.

Literature Source:
1. Abadi et al. (2016), "Deep Learning with Differential Privacy."
2. Anil et al. (2022), "Large-Scale Differentially Private BERT."

## Methods

| Name | Description |
| --- | --- |
| [solve](#mlsysim.solvers.ResponsibleEngineeringModel.solve) | Calculates the overhead of responsible engineering practices. |

### solve { #mlsysim.solvers.ResponsibleEngineeringModel.solve }

```python
solvers.ResponsibleEngineeringModel.solve(
    base_training_time,
    epsilon=1.0,
    delta=1e-05,
    min_subgroup_prevalence=0.01,
)
```

Calculates the overhead of responsible engineering practices.