AIGP-M2 – AllNet Law

This multiple choice assessment focuses on the AIGP body of knowledge

Format: Multiple Choice

The result will be provided immediately, with details on all questions.

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1. Consider you are a privacy consultant with a tech company developing an AI-driven health-monitoring application. The application collects user data to provide personalized health insights. The company is dedicated to ensuring user privacy and seeks your advice. In the context of developing a privacy-enhanced AI health-monitoring application, which of the following measures would be most effective in aligning with privacy standards?

A) Implementing user consent mechanisms and anonymizing collected data to protect user identities.

B) Utilizing social media integration to enrich user profiles with additional lifestyle information and offers.

C) Sharing raw user data with various pharmaceutical companies to facilitate targeted drug development.

D) Storing user health data on a publicly accessible server for easy collaboration with health care researchers.

2. Which of the following pairs provides examples of potential group harms associated with AI technologies?

A) Safety and deep fakes.

B) Acceleration and reputational.

C) Mass surveillance and facial recognition.

D) Carbon dioxide emission and agricultural.

3) WonderTemp, a temporary staffing company, terminated most of its employees after implementing a virtual assistant to help write emails, policies, marketing materials and responses to client questions. Three months later, WonderTemp learns that four of its biggest clients are not renewing their contracts because the work product from WonderTemp is full of inaccuracies. Based on these facts, what types of harm has WonderTemp suffered because of its reliance on the virtual assistant?

A) A. Legal and regulatory harm.

B) Individual and societal harm.

C) Economic and reputational harm.

D) Disinformation and reportable harm.

4) Why should an Al developer separate data into training and test sets?

A) To create models that can be trained quickly.

B) To reduce the inherent biases in stochastic gradient descent.

C) To avoid overfitting to the specific characteristics of the training data.

D) To improve the test validation scores by presenting multiple datasets.

5) Which of the following is a key element in ensuring responsible data governance when deploying an Al model?

A) Storing all data indefinitely to allow future analysis

B) Implementing data minimization and retention limits.

C) Only focusing on data quality without considering privacy.

D) GPU performance, to evaluate the tool's robustness.

6) All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?

A) Risk probability/severity.

B) Adversarial robustness.

C) Statistical sampling.

D) Decision analysis.

7) Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible Al?

A. Conducting an assessment of existing compliance programs to determine overlaps and integration points.

B. Employing a new software platform to modernize existing compliance processes across the organization.

C. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.

D. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.

8) Which statement best describes the primary objective of implementing a comprehensive Responsible AI (RAI) framework within an enterprise setting?

A) To align the design, development, and deployment of AI systems with core ethical principles, legal requirements, and organizational values while systematically mitigating associated risks (e.g., bias, transparency failures) and fostering stakeholder trust.

B) To focus solely on maximizing profit through the use of AI, even if it leads to unintended negative consequences for some individuals or groups.

C) To delegate all ethical considerations for AI to the legal department, treating it primarily as a compliance issue rather than a fundamental design principle.

D) To prioritize the development of the most advanced AI capabilities, assuming that potential risks can be addressed retroactively after deployment.