AI Associate Ethical Considerations of AI Practice Question
A global retail company deploys an AI-powered chatbot for customer service. The chatbot uses natural language processing to understand and respond to customer inquiries. After deployment, the company notices that the chatbot consistently provides less accurate and less helpful responses to customers from non-English-speaking regions, particularly those using dialects or slang. The company's data science team trained the model primarily on English-language customer service transcripts from the US and UK. The AI Ethics team has raised concerns about fairness and potential bias. The company wants to address this issue while maintaining overall performance and minimizing cost. Which action should the company take first?
⚠ Common exam trap
Salesforce often tests the principle that measurement and diagnosis must precede intervention; the trap here is that candidates may jump to a technical fix (like a fairness constraint) or a drastic operational change (like disabling the chatbot) without first conducting the essential diagnostic step of a fairness audit.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Conduct a fairness audit using diverse test cases from multiple languages and dialects to quantify the disparity.
The first step in addressing potential bias in an AI system is to measure and quantify the disparity. Conducting a fairness audit with diverse test cases from multiple languages and dialects provides the data science team with a clear, empirical baseline of the model's performance gaps. This diagnostic step is essential before any remediation (like retraining or adding constraints) to ensure that subsequent actions are targeted and effective, avoiding wasted resources or unintended consequences.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement a fairness constraint in the model's loss function to penalize disparities across language groups.
Why it's wrong here
Implementing constraints without first analyzing the bias could lead to unintended trade-offs or new biases.
- ✓
Conduct a fairness audit using diverse test cases from multiple languages and dialects to quantify the disparity.
Why this is correct
An audit with diverse test cases will identify the specific gaps, allowing targeted and cost-effective improvements.
- ✗
Disable the chatbot for non-English languages and redirect those customers to human agents.
Why it's wrong here
Restricting service to English only fails to serve non-English-speaking customers equitably.
- ✗
Collect more training data from all regions and retrain the model from scratch.
Why it's wrong here
Collecting more data without first understanding the specific gaps may not efficiently address the dialect/language issue and could be costly.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.