AIF-C01 Question 164
Select 3An organization is developing an AI system to provide loan recommendations. They want to ensure the system adheres to responsible AI principles. Which of the following practices should they prioritize to mitigate bias and promote fairness in their AI model?
- A
Use a diverse dataset that represents all demographic groups.
- B
Avoid documenting the data collection process to save time.
- C
Regularly test the model for disparate impact across user groups.
- D
Ensure the system makes decisions without any human oversight.
- E
Conduct explainability tests to understand why the AI model makes specific decisions.
Show answer and explanation
Correct answers: A, C, E
Explanation
Responsible AI emphasizes practices like mitigating bias, ensuring fairness, and increasing transparency. Using a diverse dataset, testing for disparate impact, and conducting explainability tests are all critical steps to ensure the AI system makes fair and unbiased decisions. These practices also align with the principles of inclusivity and safety in AI development.
- A. Correct.
Using a diverse dataset ensures that the model learns from a wide range of demographic groups, reducing the risk of bias in its predictions. This is a key practice for promoting fairness.
- B. Incorrect.
Avoiding documentation of the data collection process can lead to a lack of transparency and hinder efforts to address potential biases. This practice does not align with responsible AI principles.
- C. Correct.
Regularly testing the model for disparate impact is essential for identifying and addressing unfair outcomes that may affect specific groups, ensuring fairness.
- D. Incorrect.
While automation is valuable, responsible AI often requires human oversight to address ethical concerns and ensure the system operates as intended. Completely removing human oversight is not a responsible approach.
- E. Correct.
Conducting explainability tests helps stakeholders understand the rationale behind AI decisions, promoting transparency and trust in the system. This aligns with responsible AI principles.