In this enlightening episode, we delve deeper than the usual buzz surrounding AI’s perils, focusing instead on the tangible problems emerging from the use of machine learning algorithms across Europe. We explore “suspicion machines” — systems that assign scores to welfare program participants, estimating their likelihood of committing fraud. Join us as Justin and Gabriel share insights from their thorough investigation, which involved gaining access to one of these models and meticulously analyzing its behavior.

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Featuring:

• Gabriel Geiger – X
• Justin-Casimir Braun – X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

• Article - “Inside the suspicion machine”
• The methodology behind Justin and Gabriel’s report

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