Ofcom: guidance on highly effective age assurance (Part 3) · 6. Technical glossary

6. Technical glossary Metrics used to measure the accuracy of age assurance

metrics used to measure the accuracy of age assurance 2 · in force: unknown

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6. Technical glossary Metrics used to measure the accuracy of age assurance

| Term | Meaning | | | | --- | --- | --- | --- | | Outcome / error parity | | Outcome / error parity is a measure designed to compare | | | | | how an age assurance process outcome impacts users in | | | | | different groups, both positively and negatively, and/or how | | | | | often these different groups of users are subjected to errors. | | | Standard deviation (SD) | A measure of variation or dispersion of the dataset relative to the mean. A low SD suggests datapoints closer to the mean, whereas a high SD suggests datapoints are more dispersed. s = ∑((X − MAE)^2/(n – 1)) where X = is the ith point in the dataset, MAE = is the mean absolute error, and n = the number of datapoints in the dataset. | | | | True positives (TP) | | An outcome where a model correctly predicts a positive class | | | | | i.e., a user is under 18 and model predicts their age as under | | | | | 18. | | | True positive rate (TPR) / Recall | For the purpose of age assurance, this measures the proportion of TP predictions out of all actual positive instances (i.e., TP and FN). This metric highlights the model’s performance in correctly identifying positive cases. The formula is TPR = TP / (TP + FN). | | |

Outcome / error parity different groups, both positively and negatively, and/or how

s = ∑((X − MAE)^2/(n – 1)) where X = is the ith point in the

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| Term | Meaning | | |
| --- | --- | --- | --- |
| Outcome / error parity | | Outcome / error parity is a measure designed to compare | |
| | | how an age assurance process outcome impacts users in | |
| | | different groups, both positively and negatively, and/or how | |
| | | often these different groups of users are subjected to errors. | |
| Standard deviation (SD) | A measure of variation or dispersion of the dataset relative to the mean. A low SD suggests datapoints closer to the mean, whereas a high SD suggests datapoints are more dispersed. s = ∑((X − MAE)^2/(n – 1)) where X = is the ith point in the dataset, MAE = is the mean absolute error, and n = the number of datapoints in the dataset. | | |
| True positives (TP) | | An outcome where a model correctly predicts a positive class | |
| | | i.e., a user is under 18 and model predicts their age as under | |
| | | 18. | |
| True positive rate (TPR) / Recall | For the purpose of age assurance, this measures the proportion of TP predictions out of all actual positive instances (i.e., TP and FN). This metric highlights the model’s performance in correctly identifying positive cases. The formula is TPR = TP / (TP + FN). | | |

Outcome / error parity different groups, both positively and negatively, and/or how

s = ∑((X − MAE)^2/(n – 1)) where X = is the ith point in the

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Definitions detail outcome parity, standard deviation, and true positive rate metrics used in evaluating age assurance models.

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Instrument: uk:ofcom/heaa-guidance-part3
Part: 6. Technical glossary
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Source: ofcom.org.uk/siteassets/resources/documents/consultations/category-1-10-weeks/statement-protecting-children-from-harms-online/main-document/guidance-on-highly-effective-age-assurance-part-3-guidance-.pdf?v=396471