Kedis ku xidhan hagaha · Adag Heerka

AI ee Kediska Maamulka Faa'iidooyinka Dawladda

Tijaabi fahamkaaga Robodebt iyo fadeexadda daryeelka ilmaha ee Nederland, dhibaatada heerka-saldhigga ee moodooyinka khiyaanada, iyo ilaalinta algorithms faa'iidada.

Wadooyinka haga ee la xidhiidhaAI ee Maamulka Faa'iidooyinka Dowladda
Su'aal 1 ee 8

Maxay ahayd cilladda xisaabinta xudunta u ah qorshaha Robodebt ee Australia?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Maxay ahayd cilladda xisaabinta xudunta u ah qorshaha Robodebt ee Australia?
    • It used facial recognition to identify claimants
    • It averaged annual tax-office income evenly across fortnights
    • It relied on a neural network trained on social media
    • It randomly selected recipients for debts
  2. Hadalkee ku saabsan tignoolajiyada Robodebt sax ah?
    • It was an advanced deep-learning system
    • It was a chatbot that negotiated debts
    • It used satellite data to verify addresses
    • It was a flawed formula and data-matching process, not sophisticated AI
  3. Arrinkee ku jira qaabka khatarta ah ee hay'adda cashuuraha Nederland ayaa dhaleeceyn gaar ah u keentay?
    • Nationality
    • Preferred language of correspondence
    • Type of bank account used
    • Age of the youngest child
  4. Goorma ayay golaha wasiirada ee Nederlaan isku casileen fadeexad ku saabsan daryeelka carruurta?
    • March 2015
    • June 2018
    • January 2021
    • September 2024
  5. Maxaa ku dhacay nidaamka SyRI ee Nederland 2020?
    • It was expanded nationwide
    • A court halted it on privacy and human-rights grounds
    • It was sold to another country
    • It was converted into a chatbot
  6. With 1 percent fraud, 90 percent detection and a 5 percent false-flag rate, what is true of flagged people?
    • Almost all are fraudsters
    • Exactly half are fraudsters
    • None are innocent
    • Most flagged people are innocent
  7. Why can labels from past investigations bias a fraud model?
    • Investigations are always random
    • Labels are encrypted
    • The model learns where investigators looked, not where fraud actually is
    • Labels are too few to train on
  8. Which design is recommended for risk scores in benefits systems?
    • A score triggers human review, never an automatic debt or suspension
    • High scores automatically cancel payments
    • Scores are kept secret from caseworkers
    • Scores replace the appeals process