Mitigating Risks

The initial risk assessment involves devising strategies that will reduce the impact of the risks that have been identified. Some examples include:

1. Reviewing the quality of the data set:

Bias and incomplete data sets heavily influence discriminatory outcomes – it is important to be wary of systemic practices and how social implication may affect the data (e.g. overrepresentation of Indigenous people in crime) and ensuring the data holistically represents specific categories. Human oversight and judgement is essential.

2. Monitoring for data drift:

Poor model accuracy could be the result of target variables and independent variables changing overtime – it is important to consistently monitor these variables and record them, additionally think about what impact any discrepancies between variables may have.