When using AI to fully automate the process of deciding client loan rates, the primary concern from a privacy perspective is model explainability.
Why Model Explainability is Critical:
Transparency: It ensures that the decision-making process of the AI model can be understood and explained to stakeholders, including clients.
Accountability: Helps in identifying biases and errors in the model, ensuring that the AI is making fair and unbiased decisions.
Regulatory Compliance: Various regulations require that decisions, especially those affecting individuals' financial status, can be explained and justified.
Trust: Builds trust among users and stakeholders by demonstrating that the AI decisions are transparent and justifiable.
Other options, such ascredential theft, prompt injections, and social engineering, are significant concerns but do not directly address the privacy and fairness implications of automated decision-making.
[References:, CompTIA SecurityX Study Guide, "The Importance ofExplainability in AI," IEEE Xplore, GDPR Article 22, "Automated Individual Decision-Making, Including Profiling", , , , , , ]
