6 Practical tips to avoid unlawful discrimination when using AI The following are practical tips for insurers to help minimise the risks of a successful discrimination claim when using AI for insurance pricing and underwriting: 3. Ensure customers have a way to understand why higher premiums may apply and what they can do to reduce their risk exposure and hence their premiums. 1. Consider carefully whether (and how) protected attributes are likely to be related to risk for the type of insurance at hand. These considerations should (where possible) be based on data. If such a relationship is likely: 4. Document decisions around insurance pricing, including the reasons for those decisions. This documentation will be helpful in explaining to the court, if necessary, why such decisions were made and why the insurer considers that they are reasonable. Such documentation may also be a valuable risk management tool, allowing greater transparency and understanding of pricing decisions within the insurer, which in turn may help identify any risks of unlawful discrimination. a. For protected attributes with an insurance exemption, collect data on which it is reasonable to rely, and base any discrimination upon that data, in accordance with the requirements of the data exemption. If such additional data cannot be reasonably obtained, consider whether the no data exemption might apply. b. For protected attributes without an insurance exemption, the protected attribute should not be used directly in price setting. An insurer should also take suitable steps to ensure its prices are reasonable and not indirectly discriminatory. This may include use of that protected attribute within underlying pricing models, where data is available, to test for or mitigate against indirect discrimination. 2. Check data for representativeness, accuracy, errors, omissions or other issues. Model outputs that are used as input data for insurance risk models should be checked similarly. If issues are identified with the data: a. Data may be preprocessed to address certain issues such as missing values or errors. 5. Where appropriate, give reasons to customers for decisions. Explaining the reasons for a decision may help a customer understand why the insurer considers that its decision was not discriminatory, and potentially prevent a claim of discrimination being brought at all. An option for human review of automated decisions may also be a useful risk mitigation strategy for various issues, including discrimination. 6. Test and monitor models and their outputs. Test prices (wherever possible) to assess whether they might give rise to claims of indirect discrimination, particularly whether such pricing decisions would be considered reasonable in the circumstances. Monitoring processes may include automated and human routines. 7. Ensure relevant decision-making staff are suitably trained in concepts of discrimination. 8. An insurer should seek legal advice where it is unsure of the correct course of action and is concerned about breaching anti-discrimination legislation. b. An insurer might consider obtaining more or different data, if there are issues of representativeness, biases in accuracy, or other structural issues which may disproportionately impact protected groups. Guidance Resource: Artificial intelligence and discrimination in insurance pricing and underwriting • 2022 • 39