ISACA’s AI IQ Quiz

AI Fundamentals Course: What is the primary goal of Artificial Intelligence?

1. To create machines that can only perform repetitive tasks

   AI is designed to perform a wide range of tasks, not just repetitive ones.

2. To replace human intelligence entirely

   AI aims to augment human intelligence, not replace it entirely.

3. To simulate human intelligence in machines

   The primary goal of AI is to simulate human intelligence in machines to perform tasks that typically require human intelligence.

4. To eliminate the need for human decision-making

   AI is intended to assist and enhance human decision-making, not eliminate it.

AI Governance Course: A company is implementing AI systems to enhance its decision-making processes. Which is the most critical first step in ensuring effective AI governance?

1. Focusing on AI system performance metrics

   Focusing on performance metrics is important but secondary to establishing clear governance policies.

2. Investing in advanced AI technologies

   While investing in advanced AI technologies is important, it is not the first step in establishing effective AI governance.

3. Training employees on AI ethics

   Training employees on AI ethics is valuable but should follow the establishment of foundational governance policies.

4. Establishing clear data governance policies

   Clear data governance policies are foundational for ensuring that AI systems operate with privacy, confidentiality, and compliance in mind.

Ethical Perspectives in AI Course: A company is developing an AI system for hiring decisions. During testing, it is discovered that the AI favors certain demographic groups over others due to biased training data. What should the company do to address this ethical issue?

1. Ignore the bias since it reflects real-world data.

   Ignoring the bias perpetuates discrimination and violates ethical principles in AI development.

2. Retrain the AI with more diverse and representative data.

   Retraining the AI with diverse data helps reduce bias and ensures fairer decision-making.

3. Limit the AI's use to non-critical hiring decisions

   Limiting the AI's use does not address the underlying bias and may still lead to unethical outcomes.

4. Replace the AI system with human decision-makers

   While human decision-makers can help, addressing the bias in the AI system is crucial for ethical AI development.

AI Threat Landscape Course: A company deploying an AI system for customer service discovers that the system is vulnerable to data manipulation by external actors. Which risk factor does this scenario primarily highlight?

1. Likelihood

   Likelihood refers to the probability of an event occurring, not the exposure of the system to threats.

2. Vulnerability

   Vulnerability refers to weaknesses in the processes used to develop or deploy AI systems, which could expose them to threats.

3. Impact

   Impact refers to the potential consequences or damage caused by an event, not the exposure to threats.

4. Opportunity

   Opportunity refers to the resources and positioning required for threat actors to carry out actions, not the weaknesses in the system.

Machine Learning for Business Enablement Course: What is the primary function of a neural network in machine learning?

1. To mimic the human brain in processing information

   Neural networks are designed to mimic the human brain's ability to process and learn from information.

2. To generate random predictions for datasets

   Neural networks aim to make accurate predictions based on patterns in the data, not random predictions.

3. To store large amounts of data for analysis

   Neural networks are not primarily used for data storage but for processing and analyzing data.

4. To replace traditional databases in machine learning systems

   Neural networks complement traditional databases but do not replace them.

Machine Learning, Advanced Course: A company wants to use machine learning to predict customer churn based on past behavior. Which aspect of neural networks makes them suitable for this task?

1. Their ability to identify complex patterns in data

   Neural networks excel at identifying complex patterns in data, making them suitable for predicting customer churn.

2. Their capability to store large amounts of customer data

   Neural networks are not primarily used for data storage but for analyzing and learning from data.

3. Their ability to replace traditional databases

   Neural networks do not replace databases but work alongside them to analyze data.

4. Their speed in processing simple calculations

   While neural networks can process data efficiently, their strength lies in identifying patterns, not simple calculations.

Introduction to AI for Auditors Course: Why is it essential for auditors to understand the lifecycle of AI systems from development to deployment?

1. To ensure AI systems meet compliance and ethical standards.

   Auditors need to understand AI's lifecycle to evaluate whether systems adhere to compliance and ethical requirements.

2. To develop AI systems for industrial use.

   Auditors are not responsible for developing AI systems; their role is to assess and audit them.

3. To replace traditional auditing methods with AI tools.

   While AI can support auditing, it does not replace traditional methods entirely.

4. To predict future trends in AI technology.

   Predicting trends is not the primary focus of auditors; their role is to assess existing systems.

Introduction to AI for Auditors Course: An auditor is reviewing a company's financial transactions using an AI-powered tool. The tool flags a series of transactions as potentially fraudulent based on patterns it has learned from historical data. What should the auditor do next?

1. Rely solely on the AI tool's judgment and proceed with the audit without further review.

   The auditor should not rely solely on the AI tool and must validate its findings

2. Manually review the flagged transactions to confirm if they are indeed fraudulent.

   The auditor must verify the flagged transactions to ensure they are truly fraudulent before taking further action.

3. Immediately report the flagged transactions as fraud to the authorities.

   The flagged transactions need to be reviewed and verified before reporting to the authorities.

4. Ignore the flagged transactions as the AI tool might have made an error.

   The flagged transactions could lead to missing critical fraudulent activities.

Auditing Generative AI Course: What is a significant risk that must be addressed during the auditing process of AI systems?

1. Over-reliance on manual processes

   Over-reliance on manual processes is not directly relevant to the risks associated with AI systems, as it pertains more to traditional workflows.

2. High computational costs

   While high computational costs can be a challenge in AI systems, it is not a primary risk that needs to be addressed during the auditing process.

3. Lack of user interface design

   A lack of user interface design is not a significant risk in the context of AI auditing, as it pertains more to usability than to systemic risks like bias.

4. Bias in AI systems

   Bias in AI systems is a well-documented and significant risk that can lead to unfair or discriminatory outcomes if not addressed.

Auditing Generative AI Course: Why is it important to define the audit scope in a GenAI audit?

1. To ensure the inclusion of critical processes, systems, and business functions, and to focus on critical risks.

   It directly aligns with the stated importance of a well-defined audit scope in a GenAI audit.

2. To reduce the time spent on the audit by excluding non-critical systems.

   While efficiency is a benefit, the primary purpose of defining the scope is not to reduce time but to ensure critical elements are included.

3. To ensure compliance with all organizational policies and procedures.

   Defining the audit scope is not primarily about compliance with policies but about focusing on critical processes and risks.

4. To identify all potential risks within the organization.

   The audit scope focuses on critical risks, not all potential risks within the organization