# Testing software for non-discrimination: an updated and extended audit in the Italian car insurance domain

arXiv: https://arxiv.org/abs/2502.06439 · HTML: https://mlsanity.com/paper/2502.06439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Fairness Testing: Testing Software for Discrimination](https://mlsanity.com/paper/1709.03221/) (2017)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies](https://mlsanity.com/paper/2109.06974/) (2021)
- [Auditing Work: Exploring the New York City algorithmic bias audit regime](https://mlsanity.com/paper/2402.08101/) (2024)
- [Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness](https://mlsanity.com/paper/2403.20089/) (2024)
- [Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing](https://mlsanity.com/paper/2105.10174/) (2021)

## Abstract (arXiv)

Context. As software systems become more integrated into society's infrastructure, the responsibility of software professionals to ensure compliance with various non-functional requirements increases. These requirements include security, safety, privacy, and, increasingly, non-discrimination. Motivation. Fairness in pricing algorithms grants equitable access to basic services without discriminating on the basis of protected attributes. Method. We replicate a previous empirical study that used black box testing to audit pricing algorithms used by Italian car insurance companies, accessible through a popular online system. With respect to the previous study, we enlarged the number of tests and the number of demographic variables under analysis. Results. Our work confirms and extends previous findings, highlighting the problematic permanence of discrimination across time: demographic variables significantly impact pricing to this day, with birthplace remaining the main discriminatory factor against individuals not born in Italian cities. We also found that driver profiles can determine the number of quotes available to the user, denying equal opportunities to all. Conclusion. The study underscores the importance of testing for non-discrimination in software systems that affect people's everyday lives. Performing algorithmic audits over time makes it possible to evaluate the evolution of such algorithms. It also demonstrates the role that empirical software engineering can play in making software systems more accountable.