引导式测验 · 中等 等级

房地产中的人工智能测验

通过将估计与判断分开并审查数据、代理风险、细分市场、隐私和监督来评估房地产人工智能。

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问题 1 的 8

A model estimates a property’s value for internal planning. What should not be assumed?

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  1. A model estimates a property’s value for internal planning. What should not be assumed?
    • That the estimate is a neutral appraisal or appropriate for every decision
    • That it has a date
    • That it used property data
    • That it needs documentation
  2. Why define the property, market, and date before evaluating an estimate?
    • Every property uses the same reference market
    • Value depends on market context and the decision being made
    • A date changes only the display format
    • The model always predicts sale price
  3. A model uses postal code because it predicts historical price well. What should reviewers consider?
    • Whether the code has five digits
    • Only whether it improves average error
    • Whether it proxies for protected characteristics or historical segregation
    • Whether the model is explainable by name
  4. Why evaluate errors across neighborhoods and property types?
    • It eliminates interest-rate changes
    • All groups have identical data quality
    • The average guarantees fairness
    • A citywide average can hide systematic differences
  5. What should be monitored after deployment?
    • Only the vendor’s marketing page
    • Listings, interest rates, and data coverage changes
    • The model’s logo
    • A single citywide score
  6. What data does the real-estate guide say to protect?
    • Only the model’s weights
    • Only public house styles
    • Applicant, tenant, owner, and location information
    • The office phone number
  7. A property recommendation may steer or exclude applicants. What does the guide recommend?
    • Provide oversight and a route to correct inaccurate data
    • Let the score silently decide
    • Remove the correction path
    • Use the same threshold without review
  8. Before relying on an automated housing outcome, what should a practitioner do?
    • Skip privacy review
    • Trust a high score alone
    • Assume the same rules apply everywhere
    • Document the model, data, vendor, threshold, and human action and consult qualified experts