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Sztuczna inteligencja do podsumowań zeznań i quizu przygotowawczego

Testuje trzy typy podsumowań zeznań, sprawdza, dlaczego zachowanie numerów stron i wierszy ma znaczenie dla sztucznej inteligencji oraz jak sprawdzać niespójności oznaczone przez sztuczną inteligencję w zeznaniach.

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Pytanie 1 z 8

Which type of deposition summary groups testimony by issue rather than following transcript order?

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Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.

  1. Which type of deposition summary groups testimony by issue rather than following transcript order?
    • Page-line summary
    • Narrative summary
    • Errata summary
    • Topical summary
  2. Why does the guide warn about feeding AI a rough draft transcript?
    • It may contain errors the model will repeat in the summary
    • AI tools cannot read rough drafts
    • Rough drafts are always privileged
    • Rough drafts lack any speaker labels
  3. What is a common false positive when AI searches for inconsistencies in testimony?
    • Flagging a date written in a different format
    • Missing every contradiction in the transcript
    • Treating a clarified answer as a contradiction
    • Refusing to compare more than one witness
  4. Why do protective orders matter when uploading transcripts to an AI vendor?
    • They require AI summaries to be filed with the court
    • They may limit who can receive confidential testimony, so the upload must fit their terms
    • They ban all electronic copies of transcripts
    • They only apply to video depositions
  5. What must be used when impeaching a witness with prior testimony found through AI?
    • The AI's summary of the testimony
    • A paraphrase approved by the court reporter
    • The rough draft transcript
    • An exact quote from the certified transcript with page and line
  6. What happens when page and line numbers are stripped from a transcript before AI processing?
    • The model runs faster and cites more accurately
    • The court reporter must recertify it
    • The model has to guess locations, producing wrong citations
    • Speaker labels are automatically restored
  7. What risk does chunking a long transcript create?
    • It can separate a question from its answer or an answer from a later correction
    • It permanently deletes objections from the record
    • It changes the witness's words
    • It prevents any summary from being generated
  8. For inconsistency searches, what output format does the guide recommend requesting?
    • A single sentence verdict on credibility
    • Both conflicting passages side by side
    • A percentage score of witness honesty
    • Only the later of the two statements