The Impact on Women on the Removal of Gender as a Rating Variable in Motor-Vehicle Insurance
Bibliographic record
Abstract
Insurers use actuarial statistics as rating variables to differentiate and distinguish for the purposes of risk classification. They justify their use of actuarial statistics due to its accuracy as a predictor of risk. South African motor-vehicle insurers use gender, inter alia, as a rating variable to classify risks into certain classes and to determine insurance premiums. Depending upon whether the insured is male or female, it could have a significant impact on the cost of his or her premium. Women drivers pay less for motor-vehicle insurance because actuarial statistics indicate that women are more careful drivers and are involved in 20 per cent fewer accidents than men. Men pay higher premiums because the statistics indicate that they are less responsible drivers than women. Should a South African court decide that the use of gender as a motor-vehicle insurance rating variable is unfair discrimination, this would benefit male drivers, as it would lower their premium. Women, on the other hand, would be disadvantaged as they would be required to pay higher premiums to subsidise men. The article examines the impact that the removal of gender as a rating variable in motor-vehicle insurance would have on women, and asks if the effects thereof would influence a South African Court’s decision in determining if the use of gender as a rating variable amounts to unfair discrimination. The article first considers the findings of American and Canadian Courts in determining this same issue and then considers South African equality legislation, particularly the Promotion of Equality and Prevention of Unfair Discrimination Act 4 of 2000 (“the Equality Act”). Thereafter, the article provides recommendations for a South African Court. As the Equality Act indicates that the discriminatory insurance practice of placing a disadvantage or advantage on persons based inter alia on their gender may possibly be unfair, it is suggested that South African insurers would have to consider alternative methods of risk assessment. In the light of the American and the Canadian case law, the article suggests that there should be a change of approach to insurance risk assessment. Rather than using gender as a rating variable the insurer could assess the risk of the individual insured, using appropriate, neutral rating variables suited to the particular circumstances of the insured. This would require a much more intensive and individualised risk evaluation and would require the insurer to “tailor-make” insurance for each individual. It is submitted that such an approach would give effect to the right to equality by disallowing the use of gender as a rating variable without producing the undesirable consequence that women drivers would have to subsidise men.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".