“We must clearly define the objective that the Family Allowance Fund wishes to achieve with its algorithm” | EUROtoday

PTo successfully fight unlawful practices, supervisory authorities are always integrating new applied sciences. Currently, they’re turning an increasing number of in direction of algorithms. But, as highlighted by the survey revealed on December 4, 2023 by The world on the observe of focusing on by the Family Allowance Fund (CAF), or the debates round the usage of identification programs through the subsequent Olympic Games in Paris, the usage of these applied sciences can generate a sure variety of issues .

The phenomenon is especially the case when it impacts populations in weak conditions. If the issues raised by the investigation World are above all moral and ethical, the financial perspective may also present perception that we provide right here.

The constraints, significantly budgetary ones, which weigh on the establishments accountable for detecting fraud push them to undertake a set of measures to maximise their effectiveness, particularly the focusing on of controls, somewhat than utterly random controls. This focusing on, operated by people or algorithms, is mostly primarily based on a set of observable parameters reflecting a better likelihood of fraud.

Thus, alcohol controls are strengthened on Saturday evenings round occasion venues, and are much less frequent through the week. Likewise, the CAF has little or no management over teams whose threat of fraud is unlikely or zero, equivalent to, in fact, non-beneficiaries of the CAF.

The use of delicate parameters might give rise to discrimination. For this, a sure variety of standards are already prohibited in France, equivalent to ethnicity or gender. The Court of Justice of the European Union (CJEU) has simply dominated, Thursday, December 7, 2023, that any decision-making that makes use of ranking programs utilizing private information is unlawful.

Statistical correlations

However, a protected parameter could be not directly approximated utilizing approved standards because of statistical correlations. For instance, shoe measurement might point out gender, whereas neighborhood of residence might counsel ethnicity. The potential use of 1000’s of correlations thus makes it troublesome to stop the usage of protected standards. Concerning the oblique discrimination of the CAF algorithm, it’s going to subsequently be a query of estimating it, and figuring out whether or not it’s justified by a reliable goal.

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