Suspicion machines ⚙️

Practical AI: Machine Learning, Data Science, LLM

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 – Twitter
• Justin-Casimir Braun – Twitter
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

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

Something missing or broken? PRs welcome!

Chapitres

1. Welcome to Practical AI (00:00:07)

2. Suspicion machines (00:00:43)

3. Signs of bias (00:04:14)

4. How to start investigating (00:07:12)

5. Predictive analytic trends in gov. (00:09:29)

6. Can you justify using these systems? (00:11:37)

7. Requesting Rotterdam's data (00:14:24)

8. The model's capabilities (00:16:47)

9. Important features (00:20:23)

10. Understanding the labels (00:23:16)

11. Sponsor: Changelog News (00:26:50)

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12. Contract timeline (00:28:50)

13. How effective is the model? (00:31:19)

14. Public perception and reactions (00:34:21)

15. Hope for the future (00:39:58)

16. Goodbyes (00:45:29)

17. Outro (00:46:09)