Final answer:
Statistical parity is the type of fairness that does not consider individual merit but aims to achieve equal decision rates across different groups. It is contrasted with concepts like error rate parity, which focuses on fair error rates, and others that attempt to account for merit-based differences.
Step-by-step explanation:
The type of fairness that fails to address merit while maintaining accuracy is Statistical parity. This concept refers to a situation where the decision rate (the proportion of a specific outcome) is the same across groups that are defined by a protected attribute (e.g., race, gender).
While statistical parity can ensure that different groups receive decisions at the same rate, it does not take into account the merit or qualifications of individuals within those groups. For example, it means giving loans to the same proportion of applicants in two different demographic groups, ignoring whether the individuals in those groups have the same creditworthiness.
When focusing on error rates and fairness, it is important to distinguish between the two types of errors that may occur: chance error and bias. Chance error is the random error that can occur in any measurement due to unpredictable fluctuations.
Bias, on the other hand, is a systematic error that leads to a consistent deviation from the true value. In the context of fairness, we aim to minimize bias to ensure that decisions are just and do not systematically favor or disfavor certain groups.