Maswali yanayounganishwa na mwongozo · Kati Kiwango

AI katika Maswali ya Uandishi wa Mikopo

Kagua uandishi wa mikopo, ubaguzi wa wakala, arifa za hatua mbaya, data mbadala, maelezo na vikomo vya udhibiti.

Njia za mwongozo zinazohusianaAI Katika Uandishi wa Mikopo
Swali 1 ya 8

Je, mtindo wa uandishi wa mikopo wa AI hujaribu kutabiri nini kimsingi?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. Je, mtindo wa uandishi wa mikopo wa AI hujaribu kutabiri nini kimsingi?
    • Uwezekano kwamba akopaye atakuwa chaguomsingi
    • benki favorite akopaye
    • Mwelekeo wa soko la hisa
    • Umri wa mkopaji haswa
  2. 'Ubaguzi wa wakala' katika mifano ya mikopo ni nini?
    • Kutumia seva ya chelezo
    • Kipengele kinachosimama kwa ajili ya sifa iliyolindwa kama vile mbio
    • Kukodisha wakala ili kuomba mikopo
    • Inasimba data ya mteja
  3. What does an adverse-action notice communicate to a credit applicant?
    • A guarantee of future loan approval
    • A credit score that proves discrimination
    • That the creditor took an adverse action on the credit application
    • A list of every variable in every model
  4. Je, uandishi wa AI unaweza kusaidia nani ambao kadi za kawaida za alama mara nyingi hukataliwa?
    • Wakopaji matajiri tu
    • Benki zenyewe
    • Wadhibiti wa serikali
    • Waombaji wa 'faili-nyembamba' walio na historia ndogo ya jadi ya mkopo
  5. Kwa nini zana kama SHAP zinatumika katika uandishi wa mikopo?
    • Kuhusisha uamuzi kwa vipengele maalum kwa maelezo
    • Ili kuongeza kasi ya mtandao
    • Ili kuweka kiwango cha riba kuu
    • Ili kugundua virusi vya kompyuta
  6. Je, kuna mvutano gani mkuu katika kutumia data zaidi kwa uandishi wa chini?
    • Data zaidi daima hupunguza usahihi
    • Data zaidi inaweza kuboresha usahihi lakini inaweza kuficha ubaguzi
    • Data ni kinyume cha sheria kutumia
    • Data hufanya mikopo bila malipo
  7. When a creditor supplies a statement of reasons under Regulation B, what must those reasons describe?
    • A generic statement that an algorithm denied credit
    • A complete source-code listing
    • Specific principal reasons that accurately describe factors actually used in the decision
    • A promise that the applicant will later be approved
  8. Why can an alternative-data source raise fairness questions in underwriting?
    • It is always illegal to use any nontraditional data
    • It guarantees a higher credit score
    • It eliminates the need for validation
    • It may act as a proxy for protected characteristics or reflect uneven coverage