Explanation:
In situations where historical data might contain biases, it's imperative for banks to design their AI models with corrective measures that explicitly address and rectify these biases. This proactive approach to algorithmic fairness ensures that the bank's loan approval process meets ethical standards and promotes fairness, thereby preventing the propagation of past injustices and fostering trust in the AI-driven decision-making process.
A is incorrect. While minimizing the use of personal data can limit certain biases, it may fail to achieve algorithmic fairness comprehensively and may result in models that are less predictive and potentially unfair in other ways.
B is incorrect. Increasing the complexity of a model solely for predictive accuracy can exacerbate the issue of interpretability, making it more challenging to detect and rectify biases, and can impair the ability to ensure and demonstrate fairness.
D is incorrect. Focusing solely on profitability without considering the fairness of AI models can lead to unethical practices and may eventually result in reputational damage, legal repercussions, and a loss of trust from customers and the public.
Ultimate access to all questions.
Q.5666 The financial sector's reliance on complex AI models has raised concerns about algorithmic fairness. To mitigate potential biases, which action should be prioritized by a bank when utilizing AI for loan approvals?
A
Minimize the use of personal data to reduce the risk of discriminatory biases.
B
Increase model complexity to improve prediction accuracy, regardless of the interpretability.
C
Design models to explicitly correct for historical biases present in the data.
D
Seek the highest profitability regardless of the fairness aspect of the models.
No comments yet.