Microsoft Azure AI Fundamentals (AI-900)Describe AI workloads and considerationsMedium
A developer is creating an AI model to detect fraudulent transactions. The model is highly accurate on average but occasionally flags legitimate transactions from a specific, small demographic group as fraudulent, causing inconvenience. Which principle of responsible AI should the developer prioritize to address this issue?
- AAccountability
- BInclusiveness
- CTransparency
- DFairness
Show answer & explanationAnswer & explanation
Correct answer: D. Fairness
The issue describes disparate impact on a 'specific, small demographic group', which is a direct violation of the Fairness principle. Fairness ensures that AI systems do not perpetuate or amplify biases that lead to discrimination.
Why the other options are wrong
- A. Accountability concerns who is responsible for the AI's impact, not the prevention of the bias itself.
- B. Inclusiveness ensures AI systems are accessible and empower diverse users, but 'unfairly flagging' a group points more directly to Fairness.
- C. Transparency would help understand why the model makes these decisions, but Fairness is the principle that directly addresses the biased outcome.
Responsible AI: Fairness
Ensuring AI systems treat all individuals and groups equitably, avoiding biased outcomes.
- Prevents discrimination based on protected attributes (e.g., race, gender, socioeconomic status).
- Requires careful consideration of training data and model evaluation.
- Aims for equitable access and outcomes for all users.
Memory trick: AI must be FAIR in its judgments, like a just judge.