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Proxy discrimination occurs when a seemingly neutral variable (e.g., zip code) serves as a proxy for a protected characteristic, such as race, gender, or socioeconomic status. In this case, the AI uses zip codes to determine learning support for racial segments, introducing unintentional racial bias because zip codes often reflect historical segregation and economic disparities.

Since historical housing policies (e.g., redlining) led to racial and economic segregation, using zip codes as a decision factor unfairly correlates race with access to educational resources—a classic example of proxy discrimination.

Algorithmic Bias & Fairness (FAT/ML Guidelines) – AI systems must avoid using proxies that reinforce systemic discrimination.

Civil Rights Laws (Fair Housing Act, Title VI of the Civil Rights Act) – Policies that disproportionately harm racial groups violate anti-discrimination laws.

ACM & IEEE Ethics in AI – Developers must mitigate biases in AI models that result in unfair treatment.

Deontological Ethics & Fairness – AI systems should not perpetuate historical biases through indirect discrimination.

Relevant Ethical References in Technology:Thus, the correct answer is C. Proxy discrimination, as zip codes indirectly introduce racial bias into the AI system.

The individual sells a used television without knowing or disclosing that it is damaged, which raises two ethical concerns:

Negligence – The seller fails to inspect the product properly before selling it, which is careless and irresponsible.

Misrepresentation – The seller does not disclose the damage, leading the buyer to believe the product is in working condition.

Why These Two Options?

Negligence applies because the seller should have checked the TV before listing it.

Misrepresentation applies because the seller fails to provide accurate information, even if unintentional.

B. Breach of Contract – There is no formal contract in typical e-commerce transactions unless explicitly agreed upon.

C. Conflict of Interest – This applies when personal interests interfere with professional duties, which is not the case here.

Why Not the Other Options?Thus, the correct answers are A. Negligence and D. Misrepresentation, as they reflect the ethical failures in this situation.

Kantian Ethics – Duty to provide truthful information.

Consumer Protection Laws (e.g., U.S. FTC Guidelines).

Beauchamp, T., & Childress, J. (2019). Principles of Biomedical Ethics.

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A skewed sample occurs when historical data is biased, unrepresentative, or imbalanced, leading to discriminatory AI outcomes. In this case, the AI system was trained on 20 years of national data, which likely contained historical biases against minorities in drug and alcohol disorder treatments.

Since past law enforcement and healthcare policies have disproportionately impacted minority groups, the AI inherited these biases and began making unfair, discriminatory predictions.

Algorithmic Bias & AI Ethics (ACM/IEEE Guidelines) – AI models must be trained on diverse and representative data.

Fairness in Machine Learning (FAT/ML Framework) – Preventing bias in AI requires identifying and correcting skewed data.

Historical Bias & Systemic Discrimination – Training AI on biased past records leads to unethical and discriminatory outcomes.

Deontological Ethics (Fair AI Decision-Making) – AI developers must ensure unbiased, ethical, and fair AI outputs.

Relevant Ethical References in Technology:Thus, the correct answer is B. Skewed sample, as the AI model was trained on biased historical data, leading to discrimination.

The missing records in the device database indicate that the store needs to improve the accuracy of its data management processes.

Why Accuracy?

Data accuracy ensures that all records are correctly entered, complete, and reliable.

Manual data entry is prone to errors, including missing or incorrect records.

Implementing automated data entry or validation checks can reduce human error and ensure the integrity of the database.

A. Encryption – Protects data from unauthorized access but does not ensure correct data entry.

B. Collection – Refers to gathering data, but the issue here is with missing or incorrect entries.

C. Availability – Ensures data can be accessed but does not guarantee its accuracy.

Why Not the Other Options?Thus, the correct answer is D. Accuracy, as it directly addresses the issue of missing records.

Redman, T. C. (1998). The Impact of Poor Data Quality on the Typical Enterprise.

Tavani, H. T. (2016). Ethics and Technology: Controversies, Questions, and Strategies for Ethical Computing.

Data Management Body of Knowledge (DMBOK) (2020).

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The machine learning model unintentionally discriminates against minority job seekers because it was trained on historical hiring data that contained biases.

Why Exposing Bias in Data?

Machine learning systems reflect the biases present in their training data.

If past hiring decisions favored certain groups, the AI will replicate and reinforce those biases.

The audit revealed systematic discrimination, indicating bias in the data, not the AI itself.

B. Acting fairly toward candidates – The AI is not fair if it disproportionately excludes minority job seekers.

C. Exhibiting intent to harm applicants – The AI does not have intent; bias is an unintended consequence of flawed data.

D. Scoring low humility measurement – "Humility" is not a standard AI metric; bias exposure is the actual issue.

Why Not the Other Options?Thus, the correct answer is A. Exposing bias that exists in the data, as the AI amplifies historical discrimination in hiring.

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.

U.S. Equal Employment Opportunity Commission (EEOC) Guidelines on AI Hiring.

Floridi, L. (2013). The Ethics of Information.

References in Ethics in Technology:

Industrial espionage refers to the unauthorized theft or copying of trade secrets, proprietary information, or intellectual property from one company to another, often for competitive advantage.

Why is this Industrial Espionage?

The sibling stole proprietary source code from Employee A’s unattended laptop.

This act provides unfair competitive advantage to the sibling’s company.

Unauthorized access to trade secrets is illegal under the Economic Espionage Act (1996).

A. Nepotism – Favoring family members in hiring or promotions, but this case involves theft of intellectual property.

B. Conflict of Interest – Would apply if Employee A voluntarily shared information, but here, it was stolen.

D. Corruption – Involves bribery or unethical business practices, but this is corporate theft.

Why Not the Other Options?Thus, the correct answer is C. Industrial Espionage, as it involves theft of confidential trade secrets.

Economic Espionage Act (1996), 18 U.S.C. §§ 1831-1839.

OECD Guidelines on Corporate Ethics and Intellectual Property Protection.

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The Gramm-Leach-Bliley Act (GLBA) is a U.S. federal law that governs the privacy and security of consumer financial information. It contains three key rules:

Financial Privacy Rule – Regulates how financial institutions collect and disclose personal financial information.

Safeguards Rule – Requires financial institutions to implement security programs to protect consumer data.

Pretexting Protection Rule – Prohibits deceptive attempts to obtain personal financial information.

In this case, the Financial Privacy Rule is relevant because it provides consumers with a way to control how their financial data is shared. This rule:

Requires financial institutions to provide privacy notices detailing how they collect, use, and share customer information.

Gives customers the right to opt-out of sharing certain types of financial information with third parties.

Since the customer is concerned about their mortgage details being shared, they can use the Financial Privacy Rule to control such disclosures.

B. Pretexting – Involves identity theft or fraudulent attempts to access financial data, which is not relevant here.

C. Consumer Protection – A broad concept that does not specifically address financial data privacy.

D. Safeguards – Focuses on securing customer data from breaches, not on controlling data sharing.

Why Not the Other Options?Thus, the correct answer is A. Financial privacy as it directly addresses the customer’s concern.

Gramm-Leach-Bliley Act (1999), 15 U.S.C. §§ 6801-6809.

Federal Trade Commission (FTC) Guide to the GLBA Financial Privacy Rule.

Solove, D. J. (2020). The Privacy Law Fundamentals. IAPP.

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Computerized Provider Order Entry (CPOE) is a digital system that allows healthcare providers to enter medication orders electronically, reducing errors related to handwriting and incomplete prescriptions.

Why CPOE?

Eliminates handwriting misinterpretation errors.

Ensures complete medication details, reducing omissions.

Often integrates decision support tools, warning about drug interactions and incorrect dosages.

B. Health Information Exchange (HIE): Shares patient records across providers but does not focus on medication ordering.

C. Electronic Medical Record (EMR): Digitizes patient records but does not prevent prescription errors directly.

D. Clinical Decision Support (CDS): Helps providers make better decisions but does not replace the ordering process like CPOE does.

Why Not the Other Options?Thus, the correct answer is A. Computerized Provider Order Entry (CPOE), as it directly addresses prescription errors.

Bates, D. W. et al. (1998). Effect of Computerized Physician Order Entry and a Team Intervention on Prevention of Serious Medication Errors.

Institute of Medicine (2000). To Err is Human: Building a Safer Health System.

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Contingent workers (e.g., contractors, freelancers, or temporary employees) can provide short-term solutions for resource gaps, but they come with disadvantages.

Why is minimal internal process understanding a disadvantage?

Contingent workers do not have long-term exposure to the company's workflows, culture, or business strategies.

Training contingent workers on company-specific processes takes time and effort, reducing initial productivity.

They may not be as committed to organizational goals as full-time employees.

A. Any skills gained during employment will be lost to the company at the project's completion. – While true, companies can retain knowledge through documentation and collaboration.

C. The company may require the same worker for other projects. – This is not a disadvantage; it could be a benefit if the worker is rehired.

D. The company does not have to provide benefits throughout the project's lifecycle. – This is an advantage, not a disadvantage.

Why Not the Other Options?Thus, the correct answer is B. Internal business process understanding will be minimal given the temporary nature of the workers.

Kalleberg, A. L. (2000). Nonstandard Employment Relations: Part-time, Temporary, and Contract Work.

Harvard Business Review (2022). Managing Contingent Workers Effectively.

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