CH9_Data Ethics

What is the primary goal of data ethics?

• To maximize data collection

• To use data responsibly, securely, and legally

• To eliminate all data-related risks

• To promote business profits

What is a major risk of AI-driven decision-making?

• Reduced data storage costs

• Increased speed of data processing

• Lack of transparency and potential bias

• Eliminating the need for data governance

What is a common social risk of unethical data practices?

• Improved marketing efficiency

• Enhanced automation

• Discrimination and manipulation

• Faster data processing

What is the function of a data ethics policy?

• To provide clear rules for responsible data use

• To remove all legal obligations

• To eliminate bias from AI models

• To replace cybersecurity protocols

Which is NOT an ethical risk category in data usage for decision-making?

• Privacy risks

• Financial risks

• Discrimination risks

• Manipulation risks

What is the main focus of frameworks for mitigating ethical risks in data use?

• Increasing revenue

• Balancing innovation with compliance

• Mitigating legal liability exclusively

• Ensuring data accessibility to all employees

What does personal data protection legislation primarily aim to ensure?

• Equitable access to data

• Security of sensitive data and individual privacy

• Open-source technologies in data analytics

• Profitability of private companies

How can discrimination arise in data-driven decision-making?

• Through inadequate visualization tools

• Due to biased training datasets used in algorithms

• By enforcing overly strict data privacy regulations

• Through inefficient data processing systems

Which of the following is an example of manipulation risk?

• A system crash due to data overload

• A data visualization failing to update

• Using targeted ads to influence user behavior without full disclosure

• Enabling open access to company datasets

What is a common method to address discrimination risks in algorithms?

• Increase algorithm complexity

• Regular audits of datasets and model outputs

• Limiting the use of AI in decision-making

• Sharing all datasets with the public