AI Associate Ethical Considerations of AI Practice Questions

Explanation: Retaining protected attributes like race and age allows the model to leverage potentially legitimate medical correlations, but it requires ongoing monitoring for disparate impact to ensure fairness and compliance with regulations like HIPAA. This aligns with Salesforce's ethical AI principles of transparency, accountability, and fairness. Removing features entirely (Option B) may lose important medical context, while bucketing age (Option C) still risks bias and loss of granularity. Using the model as-is (Option D) ignores ethical responsibilities.

Explanation: The ethical issue is that the bot performs poorly for Spanish-speaking customers, leading to unfair treatment. Adding Spanish language support with a separate sentiment model and intents directly addresses this disparity by ensuring the bot can understand and respond appropriately to Spanish speakers. Option A (increasing fallback threshold) does not resolve the language-specific performance gap. Option B (collecting more Spanish data but retraining the English model) is ineffective because the English model is not suited for Spanish. Option C (disabling sentiment analysis for non-English conversations) avoids the problem rather than solving it. Therefore, Option D is the best ethical action.

Explanation: The most appropriate mitigation because it addresses the root cause of bias by ensuring the training data is representative and using algorithms designed to detect and correct unfairness. Re-sampling provides a more balanced dataset, and fairness-aware algorithms actively work to prevent discriminatory outcomes. Option B (post-processing) can help but may not be sufficient alone and does not fix the underlying data bias. Option C (accepting bias) ignores the ethical responsibility. Option D (removing gender) is ineffective because proxy variables like job history or education can still correlate with gender and perpetuate bias.

Explanation: The policy requires explainability for high-stakes AI models, such as credit scoring. The model's lack of explainability is the most significant ethical gap, as it prevents stakeholders from understanding how decisions are made, which is a core ethical principle.

Explanation: Auditing the training data for bias is the first step to identify and mitigate unfairness. Option A is wrong because increasing model complexity may exacerbate bias. Option B is wrong because adding more features without bias analysis could introduce more bias. Option C is wrong because removing the zip code feature alone does not address underlying bias in other correlated features.