DSE - Data Science Ethical Considerations in AI Questions and Answers

Question 1

A bank uses an AI model for loan approvals. It's discovered that the model denies loans to a disproportionately high number of applicants from a specific zip code, which is strongly correlated with a protected demographic group. Even though applicants' financial profiles are similar to approved applicants from other areas, their applications are rejected. What is the most likely ethical issue at play?

Accepted Answer

Algorithmic bias

Answer

This scenario describes algorithmic bias, where an AI system produces systematically prejudiced outcomes due to erroneous assumptions in the machine learning process. In this case, the zip code is acting as a proxy for a protected attribute (e.g., race), causing discriminatory outcomes even if the protected attribute itself is not used.

Question 2

Which of the following best describes the primary goal of Explainable AI (XAI)?

Accepted Answer

To allow stakeholders to understand, trust, and manage the results of an AI model.

Answer

The core purpose of Explainable AI (XAI) is to make the decision-making process of AI models, especially complex 'black-box' models, transparent and understandable to humans. This fosters trust, ensures accountability, and allows for effective auditing and debugging.

Question 3

An organization wants to train a machine learning model on sensitive user data while providing strong mathematical guarantees that the model's output will not reveal information about any single individual in the dataset. Which technique is specifically designed for this purpose?

Accepted Answer

Differential Privacy

Answer

Differential Privacy is a formal framework that provides strong, mathematically provable guarantees of privacy. It works by adding precisely calibrated statistical noise to data or query results, making it impossible to determine whether any single individual's data was included in the dataset, thus protecting individual privacy while allowing for useful aggregate analysis.

Question 4

A data science team is deploying a critical AI system for medical diagnosis. To ensure accountability for the model's predictions, which of the following is the MOST crucial practice to implement?

Accepted Answer

Establishing a clear governance framework with version control, decision-logging, and designated human oversight.

Answer

Accountability in AI requires the ability to audit, understand, and assign responsibility for a system's outcomes. A governance framework with detailed logging, model versioning, and clear lines of human oversight is essential for this. While data anonymization is vital for privacy, it does not address the accountability of the model's diagnostic decisions.

Question 5

In the context of AI fairness, what does "demographic parity" (also known as statistical parity) require?

Accepted Answer

The proportion of individuals receiving a positive outcome must be the same across different demographic groups.

Answer

Demographic parity is a group fairness metric that is satisfied if the likelihood of a positive outcome is the same across different protected groups (e.g., race, gender). It focuses solely on making the prediction rates equal, independent of the true outcomes.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE - Data Science Ethical Considerations in AI Questions and Answers