Final answer:
The negative consequence of a predictive model in real life is A: wrongly predicting a person will not be able to pay their credit card. Predictive models offer quick predictions, but inaccuracies can have significant negative impacts on individuals, such as being wrongly classified as a credit risk.
Step-by-step explanation:
Negative Consequences of a Predictive Model
The correct answer to your question is A: A model used by a lab wrongly predicts a person will not be able to pay their credit card. This is a negative consequence because if the predictive model makes an erroneous prediction, it can have severe implications for individuals. For example, a person might be denied credit or financial opportunities based on the incorrect assumption that they are a credit risk. This can lead to a variety of financial difficulties for the individual, such as the inability to take out loans, get a mortgage, or manage personal cash flow effectively.
The use of predictive models offers the advantage of rapid predictions, but the downside is the potential for making mistakes. Such mistakes can arise from several factors including bad data, flawed algorithms, or an inability to account for unexpected changes in individual behavior. Models are assessed based on their predictive ability and usefulness in the real world. If there is a mismatch between the model's predictions and real-life outcomes, the model may require adjustment or may not be suitable for future use.
It's worth noting that while other options might sound negative, they would not be considered negative consequences in the context of the model's predictive performance. A model indicating a person is in danger or a bank accurately predicting a loan default are instances where the model is performing its function correctly, even if the outcomes are unfortunate for those involved.