Discrimination-free Pricing with Privatized Sensitive Attributes

Abstract

Fairness has emerged as a critical consideration in the landscape of machine learning algorithms, particularly as AI continue to transform decision-making across societal domains. To ensure that these algorithms are free from bias and do not discriminate against individuals based on sensitive attributes such as gender and race, the field of algorithmic biasness has introduced various fairness concepts, including demographic parity and equalized odds, along with methodologies to achieve these notions in different contexts. Despite the rapid advancement in this field, not all sectors have embraced these fairness principles to the same extent. One specific sector that merits attention in this regard is insurance. Within the realm of insurance pricing, fairness is defined through a distinct and specialized framework. Consequently, achieving fairness according to established notions does not automatically ensure fair pricing. In particular, the regulatory bodies are increasingly emphasizing transparency in pricing algorithms and imposing constraints for insurance companies on the collection and utilization of sensitive consumer attributes. These factors present additional challenges in the implementation of fairness in pricing algorithms. To address these complexities and comply with regulatory demands, we propose a straightforward method for constructing fair models that align with the specific fairness criteria unique to the insurance pricing domain. Notably, our approach only relies on privatized sensitive attributes and offers statistical guarantees. Further, it does not require insurers to have direct access to sensitive attributes, and it can be tailored to accommodate varying levels of transparency as required. This methodology seeks to meet the growing demands for privacy and transparency set forth by regulators while ensuring fairness in insurance pricing practices.