Question

Why is it important to limit precise outputs in a predictive model?

A: It removes the datasets in danger of violating privacy
B: It can help users make better decisions
C: It can help limit adversarial attacks
D: It limits the ability of researchers

Answer 1

Limiting precise outputs in a predictive model is important to protect privacy, improve decision-making, and limit adversarial attacks.

Step-by-step explanation:

The importance of limiting precise outputs in a predictive model is multifaceted. Firstly, it helps remove datasets that could potentially violate privacy by revealing sensitive information about individuals. This protects the privacy rights of individuals and ensures ethical practices in data analysis.

Secondly, limiting precise outputs can help users make better decisions. By providing less precise predictions, individuals are encouraged to critically evaluate the information provided by the model and make informed choices based on their own judgment and domain knowledge.

Thirdly, limiting precise outputs can also help limit adversarial attacks. Adversarial attacks refer to techniques employed by malicious actors to manipulate or exploit predictive models. By reducing the level of precision in the outputs, it becomes more difficult for attackers to exploit vulnerabilities in the model and disrupt its functionality.