A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. The company wants…

Building a responsible AI solution, especially in a sensitive domain like lending, hinges on the core principles of fairness and transparency. Detecting imbalances or disparities in the training data is a critical first step to mitigate bias, as models learn and amplify patterns present in the data. This directly addresses the goal of minimizing negative effects on customers.

Furthermore, evaluating the model's behavior to provide transparency is essential for accountability. Stakeholders, including customers, regulators, and internal auditors, must be able to understand how decisions are made. This fosters trust and allows the company to demonstrate that its criteria are being applied fairly and responsibly, in line with ethical and legal standards.

1. Official Vendor Documentation: Amazon SageMaker Developer Guide

"Fairness and Explainability with Amazon SageMaker Clarify." This guide details how to "measure imbalances in the training data before training a model" (supporting option A) and how to "explain the behavior of a model as a whole" using techniques like SHAP to provide transparency (supporting option C). See the sections on "Pre-training Bias Metrics" and "Explainability for Models."

2. University Courseware: Stanford University

Human-Centered Artificial Intelligence (HAI)

"AI Index Report 2023." Chapter 4

"Technical AI Ethics

" discusses the growing importance and implementation of fairness and transparency evaluations. It notes that "fairness... is concerned with ensuring that ML systems do not disproportionately harm (or benefit) different groups of people

" which begins with data analysis (supporting option A).

3. Peer-reviewed Academic Publication: Barocas

S.

Hardt

M.

& Narayanan

A. (2019). Fairness and Machine Learning: Limitations and Opportunities. Chapter 1

"Introduction

" discusses how biased datasets lead to discriminatory outcomes and emphasizes the need for transparency to audit and understand model behavior. This foundational text establishes the direct link between data inspection (A) and model evaluation for transparency (C) as central to fairness. (Available via fairmlbook.org

an online resource associated with the authors' work).