Insurance, algorithmic decision-making, and discrimination
Part of ACM UMAP 2021
June 21-25, 2021
Utrecht, the Netherlands & Online
29th Conference on User Modeling, Adaptation and Personalization
Insurance companies could use algorithmic systems to set premiums for individual consumers, or deny them insurance. More and more data become available for insurers for risk differentiation. For example, some insurers monitor people’s driving behaviour to estimate risks. To some extent, risk differentiation is necessary for insurance. And it could be considered fair when, e.g., high-risk drivers pay more.
But there are drawbacks. Algorithmic decision-making could lead, unintentionally, to discrimination on the basis of, for instance, ethnicity or gender. Too much personalised risk differentiation could also make insurance unaffordable for some people. Furthermore, risk differentiation might result in the poor paying more, thereby worsening economic inequality.
We will address these issues with a hybrid half-day workshop at ACM UMAP 2021. To this workshop, we invite participants from different disciplines (for instance, computer science, law, human- computer interaction, data justice, ethics, economy).
Interdisciplinary working groups
The panel discussion is guided by questions such as:
- How should discrimination on the basis of ethnicity and other grounds be avoided?
- Can non-discrimination norms be built in the computer systems of insurers, and if so, which norms?
- How can discrimination by algorithmic systems be identified by those affected?
- Are current laws sufficient to protect fairness and the right to non- discrimination in the insurance area?
- Should poor people be protected against paying extra?
- Is it always reasonable when high-risk insurance consumers pay extra?
- Should health insurance be regulated and approached separately?
In the working groups, participants discuss questions triggered by the panel and focus, e.g., on:
- norms for digital information gathering in the insurance sector
- possible unjust effects of digital information gathering in the insurance sector
- means to identify these unjust effects
- means to prevent unjust effects
- open questions that need to be addressed to ensure responsible, fair and transparent digital information gathering in the insurance sector
- aspects where different disciplines can inform each other