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# System prepended metadata

title: Report Outline

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# Report Outline
Bias assessment
Link to experiment grid
Link to statement of work

## Outline
1. Introduction
    - Borrow from statement of work
    - Introduce thesis
        - Models implicitly learn bias from training data
        - Result in biased output
    - Introduce mitigation
        - Train classifier to change predictions based on protected characteristic data in dataset
3. Background
    - 
5. Research question (hypothesis)
    - Two research questions are explored in this project
        - Is there a difference in the level of bias w.r.t. the statistical parity fairness metric on model output?
            - Premise 1.A.: Models learn implicit bias from the dataset.
                - Hypotheses:
                    - H<sub>0</sub>: Models do not learn implicit bias.
                    - H<sub>1</sub>: Models do learn implicit bias.
                - Statistics:
                    - &alpha; = 0.05
                    - 1 - &beta; = 0.80
                    - Test: two-sided t-test
        - Is there a difference in the level of bias w.r.t. the model's architecture class?
            - Hypothesis 1.B.: (i)
            - Hypothesis 3: Architecture affects the amount of bias learnt by the model.
7. Research Design
8. Data Sets
9. Experiments
10. Results
11. Discussion
12. Conclusion
13. Recommendations
