Course Exit Survey

What is data privacy in the context of AI?

• Data privacy in AI refers to the speed of data processing by AI systems.

• Data privacy in AI is about maximizing data collection for better AI performance.

• Data privacy in AI means sharing personal information freely among AI developers.

• Data privacy in AI is the protection of personal information used by AI systems, ensuring compliance with regulations and ethical standards.

Why is data privacy important for AI entrepreneurs?

• Data privacy is essential for building trust, ensuring compliance, and protecting sensitive information.

• Data privacy is irrelevant for AI development.

• Data privacy is primarily about marketing strategies.

• Data privacy only affects large corporations, not entrepreneurs.

What are some common data privacy regulations affecting AI?

• SOX

• GDPR, CCPA, HIPAA

• FERPA

• PCI-DSS

How can AI systems ensure data privacy?

• AI systems can ensure data privacy through anonymization, encryption, access controls, and differential privacy.

• Sharing data with third parties without restrictions

• Storing data indefinitely

• Using public data without consent

What is algorithmic bias in AI?

• Algorithmic bias refers to the random errors in AI calculations.

• Algorithmic bias is the intentional manipulation of AI systems for profit.

• Algorithmic bias is the use of AI to enhance decision-making processes.

• Algorithmic bias in AI refers to the presence of systematic and unfair discrimination in the outcomes produced by AI systems.

What are the potential consequences of bias in AI algorithms?

• Unfair treatment, discrimination, inaccurate predictions, loss of trust, legal issues, and exacerbation of social inequalities.

• Enhanced user trust

• Improved accuracy in predictions

• Reduction of social inequalities

How can bias in AI be identified and mitigated?

• Bias can be ignored as it does not affect AI performance.

• Mitigating bias requires increasing the complexity of algorithms.

• AI bias can only be identified through user feedback.

• Bias in AI can be identified through data auditing and model evaluation, and mitigated by diversifying training data and using bias detection algorithms.

What role does diversity play in reducing bias in AI?

• Diversity reduces bias in AI by incorporating varied perspectives that help identify and mitigate biases.

• Diversity has no impact on AI bias whatsoever.

• Diversity complicates AI by introducing conflicting viewpoints.

• Diversity increases bias by favoring certain perspectives.

What does transparency in AI decisions mean?

• Transparency in AI decisions means keeping algorithms secret.

• Transparency in AI decisions indicates the cost of implementing AI systems.

• Transparency in AI decisions means clarity and openness about how AI systems make decisions.

• Transparency in AI decisions refers to the speed of decision-making.

Why is transparency important in AI systems?

• Transparency increases the complexity of AI algorithms.

• Transparency is irrelevant to user experience in AI.

• Transparency is important in AI systems to ensure accountability, build trust, and allow for the identification of biases.

• Transparency is only necessary for government AI systems.