AWS Certified AI PractitionerResponsible AIEasy
A healthcare organization is developing an AI model to assist with patient diagnosis. To ensure the model is fair and does not inadvertently discriminate against certain demographic groups, the data scientists are evaluating the model's performance across different patient populations (e.g., age, gender, ethnicity). Which Responsible AI concept are they primarily addressing?
- AAI fairness
- BAI interpretability
- CAI privacy
- DAI robustness
Show answer & explanationAnswer & explanation
Correct answer: A. AI fairness
Evaluating model performance across different demographic groups to prevent discrimination directly addresses AI fairness. This ensures equitable outcomes for all users.
Why the other options are wrong
- B. AI interpretability is about understanding how the model makes decisions, not necessarily its fairness across groups.
- C. AI privacy concerns protecting sensitive data, which is not the primary focus here.
- D. AI robustness deals with a model's resilience to adversarial attacks or unexpected inputs, not demographic bias.
AI Fairness
AI fairness is the principle that AI systems should produce equitable and unbiased outcomes for all individuals and demographic groups, preventing discrimination.
- Aims to prevent discrimination based on protected attributes.
- Requires evaluating model performance across diverse subgroups.
- Involves identifying and mitigating biases in data and algorithms.
Memory trick: Fair decisions build trust.