In high-stakes applications like credit risk assessment, ensuring ethical decision-making is paramount. The "human-in-the-loop" (HITL) approach, where the AI system provides advisory outputs and a human expert makes the final decision, is the highest priority. This design embeds accountability and allows for contextual judgment that an AI model cannot replicate. A human expert can override potentially biased or unfair automated recommendations, providing a critical safeguard against algorithmic discrimination and ensuring that decisions are both explainable and ethically sound. This approach directly addresses the core requirement of preventing bias at the point of decision.

A. Regularly updating the model is a standard practice for maintaining performance, but it does not inherently address ethical concerns or bias. New data can perpetuate or even introduce new biases.

B. An appeal mechanism is a crucial reactive control for redress after a decision is made, but it is not a preventative measure integrated into the initial decision-making process itself.

D. Restricting the model to "objective" financial metrics is a flawed strategy, as these metrics can act as proxies for protected characteristics (e.g., zip code correlating with race), leading to systemic bias.

1. ISACA

Artificial Intelligence Audit Framework

2023. In the section on AI Governance and Risk Management

the framework emphasizes the need for human oversight

especially for high-risk AI systems. It states

"For high-risk AI systems

human oversight should be in place to allow for human intervention to prevent or minimize harm... The final decision is made by a human." (Domain 1: AI Governance

Section 1.3 Human Oversight and Involvement).

2. High-Level Expert Group on Artificial Intelligence

Ethics Guidelines for Trustworthy AI

European Commission

2019. This foundational document lists "Human agency and oversight" as one of the seven key requirements for trustworthy AI. It specifies that for critical decisions

the system should support human autonomy

with the final determination left to a human. It recommends "human-in-the-loop (HITL)" approaches for high-stakes domains. (Chapter II

Page 14).

3. Barocas

S.

& Selbst

A. D.

"Big Data's Disparate Impact

" California Law Review

Vol. 104

2016. This academic paper explains how seemingly neutral or "objective" criteria can function as proxies for protected classes

leading to discriminatory outcomes. It demonstrates why simply restricting data inputs (as suggested in option D) is insufficient to prevent bias. (Section II.A. The Problem of Proxies

pp. 679-685). DOI: https://doi.org/10.15779/Z38G655.