Ethics in AI: Key Concepts

What is a common source of bias in AI systems?

• Biased training data

• User feedback analysis

• Randomized data selection

• Unbiased algorithms

How can biased training data affect AI outcomes?

• Biased training data has no effect on AI outcomes.

• Biased training data can result in unfair, inaccurate, or discriminatory AI outcomes.

• Biased training data improves AI accuracy.

• Biased training data guarantees fair AI results.

Which of the following is a method to reduce bias in AI?

• Using diverse training data

• Relying solely on expert opinions

• Using only synthetic data

• Ignoring data quality

What is the primary concern regarding privacy in AI?

• Privacy concerns are only related to social media.

• AI systems improve data security automatically.

• Unauthorized access to personal data and misuse of data by AI systems.

• AI does not collect any personal data.

How can data anonymization help protect privacy?

• Data anonymization increases data traceability to individuals.

• Data anonymization makes data more accessible to the public.

• Data anonymization requires sharing personal information with third parties.

• Data anonymization helps protect privacy by removing identifiable information, making it difficult to trace data back to individuals.

What is the role of consent in data ethics?

• Consent has no impact on data security.

• Consent is irrelevant in data sharing practices.

• Consent is only necessary for legal compliance.

• Consent is essential for ensuring individuals have control over their personal data and fosters trust in data practices.

Who is typically held accountable for AI decisions?

• End users of the AI

• Government regulators

• Developers and organizations responsible for the AI system.

• AI systems themselves

What is the concept of algorithmic accountability?

• Algorithms should operate without any oversight.

• The concept of algorithmic accountability is the responsibility to ensure algorithms are transparent, fair, and ethical.

• There is no need for transparency in algorithm design.

• Algorithmic accountability is about maximizing profits.

Which of the following is a challenge in holding AI accountable?

• AI decisions are always made by humans.

• Lack of transparency in AI decision-making.

• AI accountability is guaranteed by regulations.

• AI systems are always accurate.

What does transparency in algorithms refer to?

• Transparency in algorithms is about keeping their processes secret.

• Transparency in algorithms refers to the clarity and openness about their functioning and decision-making processes.

• Transparency in algorithms means they are always correct.

• Transparency in algorithms refers to their speed and efficiency.