Specificity difference (SD)
The specificity difference (SD) is the difference in specificity between the favored facet a and disfavored facet d. Specificity measures how often the model correctly predicts a negative outcome (y'=0). Any difference in these specificities is a potential form of bias.
Specificity is perfect for a facet if all of the y=0 cases are correctly predicted for that facet. Specificity is greater when the model minimizes false positives, known as a Type I error. For example, the difference between a low specificity for lending to facet a, and high specificity for lending to facet d, is a measure of bias against facet d.
The following formula is for the difference in the specificity for facets a and d.
SD = TNd/(TNd + FPd) - TNa/(TNa + FPa) = TNRd - TNRa
The following variables used to calculated SD are defined as follows:
-
TNd are the true negatives predicted for facet d.
-
FPd are the false positives predicted for facet d.
-
TNd are the true negatives predicted for facet a.
-
FPd are the false positives predicted for facet a.
-
TNRa = TNa/(TNa + FPa) is the true negative rate, also known as the specificity, for facet a.
-
TNRd = TNd/(TNd + FPd) is the true negative rate, also known as the specificity, for facet d.
For example, consider the following confusion matrices for facets a and d.
Confusion matrix for the favored facet a
| Class a predictions | Actual outcome 0 | Actual outcome 1 | Total |
|---|---|---|---|
| 0 | 20 | 5 | 25 |
| 1 | 10 | 65 | 75 |
| Total | 30 | 70 | 100 |
Confusion matrix for the disfavored facet d
| Class d predictions | Actual outcome 0 | Actual outcome 1 | Total |
|---|---|---|---|
| 0 | 18 | 7 | 25 |
| 1 | 5 | 20 | 25 |
| Total | 23 | 27 | 50 |
The value of the specificity difference is SD = 18/(18+5) - 20/(20+10) = 0.7826
- 0.6667 = 0.1159, which indicates a bias against facet d.
The range of values for the specificity difference between facets a and d for binary and
multicategory classification is [-1, +1]. This metric is not available for
the case of continuous labels. Here is what different values of SD imply:
-
Positive values are obtained when there is higher specificity for facet d than for facet a. This suggests that the model finds less false positives for facet d than for facet a. A positive value indicates bias against facet d.
-
Values near zero indicate that the specificity for facets that are being compared is similar. This suggests that the model finds a similar number of false positives in both of these facets and is not biased.
-
Negative values are obtained when there is higher specificity for facet a than for facet d. This suggests that the model finds more false positives for facet a than for facet d. A negative value indicates bias against facet a.
