Using postal codes as a primary factor is the greatest concern because it can serve as a strong proxy for protected characteristics such as race and ethnicity. This can lead to a form of systemic bias known as "digital redlining," where credit decisions are unfairly influenced by an applicant's geographic location rather than their individual financial merit. This practice can perpetuate and amplify historical patterns of discrimination, posing significant ethical and legal risks. The other factors, while needing careful handling, are direct, standard indicators of an individual's financial history and capacity to repay, which are legitimate considerations in credit scoring.

A: An applicant's loan history is a direct and highly relevant measure of their past credit behavior, making it an appropriate and standard input for credit assessment.

B: Utilizing historical credit data is the fundamental principle of credit scoring models; it is the primary and most direct data for predicting future creditworthiness.

C: An applicant's income is a direct indicator of their financial capacity to repay a loan and is a legitimate and conventional factor in credit decisions.

1. O'Neil

C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. (While a book

its concepts are foundational in university courses on AI ethics). Chapter 1 discusses how zip codes are used as proxies for race

leading to discriminatory outcomes in models for insurance

credit

and parole.

2. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0). NIST AI 100-1. Section 4.3.2

"Systemic and Human Biases

" discusses how proxy variables that correlate with protected classes can introduce harmful bias into AI systems.

3. Barocas

S.

& Selbst

A. D. (2016). Big Data's Disparate Impact. California Law Review

104(3)

671-732. Page 677 discusses how seemingly neutral variables

such as zip codes

can function as proxies for protected classes like race

leading to discriminatory outcomes that may violate anti-discrimination laws.

4. Hardt

M. (2018). CS 294: Fairness in Machine Learning. University of California

Berkeley. Lecture 1 notes discuss the sources of bias and discrimination in machine learning

including the use of proxy variables like zip codes in credit scoring as a classic example of redlining. Available from UC Berkeley course materials.