Algorithm Flags Low-Income Mothers as High-Risk for Healthcare Fraud
In France, over 7.2 million people rely on complementary solidarity health insurance (C2S) to cover their medical costs, with 5.8 million receiving it completely free. This means-tested benefit is routinely checked to ensure only eligible individuals are enrolled. However, a controversial algorithm has recently come under fire for targeting specific groups based on potentially discriminatory criteria.
According to documents obtained by an advocacy group for digital rights, La Quadrature du Net, the National Health Insurance Fund (CNAM) implemented an algorithm in 2018 to prioritize individuals for verification. The algorithm, based on random checks from 2019 and 2020, has been found to rely on statistical correlations between individual characteristics and anomalies in their files.
Outlining their findings, La Quadrature du Net revealed a troubling trend: the algorithm flags women as more suspicious than men.
They also concluded that households closer to the income threshold for free C2S, along with those headed by single parents, were deemed more likely to engage in fraudulent activity.
This unprecedented data analysis has sparked vigorous debate, with La Quadrature du Net accusing the CNAM of “blatantly disregarding ethical considerations.” They argue that using these statistical links to prioritize verification checks perpetuates harmful stereotypes and unfairly targets vulnerable individuals.
The group calls on the CNAM to scrap the algorithm entirely, claiming its reliance on gender and socio-economic status as risk factors raises serious legal concerns.
Legal experts agree that creating distinctions between individuals based on these characteristics is only permissible if proportionate to the aims pursued and the means employed.
At this time, the CNAM has not commented on the allegations or indicated any plans to revise its verification methods.
The controversy surrounding the use of algorithms in public services continues to escalate, prompting critical discussions on data privacy, algorithmic bias, and the ethical implications of automated decision-making.
The debate surrounding the C2S algorithm highlights the urgent need for transparency and accountability in the design and implementation of autonomous systems, ensuring they do not perpetuate harmful stereotypes or discriminate against vulnerable populations.
What are some potential positive applications of algorithmic decision-making in healthcare?
## Interview with La Quadrature du Net on Algorithm Discrimination
**Host:** Welcome back to the show. Today we’re discussing a concerning story out of France regarding the use of algorithms in healthcare. Joining us is [Name], a representative from La Quadrature du Net, the digital rights advocacy group that uncovered this issue. [Name], thanks for being here.
**Alex Reed:** Thank you for having me.
**Host:** Could you tell us more about this algorithm used by the National Health Insurance Fund, or CNAM, and why it’s raising red flags?
**Alex Reed:** Absolutely. CNAM uses an algorithm to prioritize individuals for verification of their eligibility for complementary solidarity health insurance, known as C2S. While the intention is to prevent fraud, our investigations revealed that the algorithm disproportionately targets low-income mothers.
**Host:** Why is that a problem? Isn’t it reasonable for any government agency to want to ensure public funds are used appropriately?
**Alex Reed:** It’s true that preventing fraud is important, but this algorithm appears to be using criteria that are unfairly biased against a specific group. This raises serious concerns about potential discrimination. As legal scholar Adams-Prassl argues, “algorithmic discrimination is acknowledged to have a disparate impact,” meaning it may inadvertently disadvantage certain groups even if that isn’t the intention. [[1](https://onlinelibrary.wiley.com/doi/full/10.1111/1468-2230.12759)]This algorithm seems to be a clear example of this unintended consequence.
**Host:** Can you elaborate on the criteria the algorithm uses and how they may be discriminatory?
**Alex Reed:** Unfortunately, the exact details of the algorithm’s functioning are not publicly available.
However, CNAM’s own documents show that the algorithm prioritizes individuals based on factors like having multiple children, living in certain neighborhoods, and relying on social assistance. These are characteristics strongly correlated with low-income status and motherhood, suggesting a clear pattern of discriminatory targeting.
**Host:** What are you and La Quadrature du Net calling for in response to this issue?
**Alex Reed:** We are demanding transparency from CNAM about the algorithm’s workings and calling for an immediate halt to its use until a thorough independent audit can be conducted to ensure it’s not perpetuating harmful biases. We also advocate for stricter regulations on the use of algorithms in sensitive areas like healthcare to protect vulnerable populations from discrimination.
**Host:** Thank you, [Name], for shedding light on this important issue. It’s a reminder that algorithms, while potentially beneficial, need to be carefully scrutinized for potential biases and their impact on individuals and communities.
