In what way can an HR firm ensure the generative AI model is more transparent in its candidate selection?

Increasing explainability through policy is essential for ensuring that generative AI models used in candidate selection are transparent. By developing and enforcing policies that prioritize explainability, an HR firm can make the decision-making processes of the AI model clearer and more understandable to all stakeholders involved, including candidates and HR professionals.

This involves creating guidelines that require the model to provide justifications for its decisions, such as highlighting the criteria that led to specific candidate evaluations or selections. Such transparency can build trust among candidates and help organizations ensure compliance with ethical standards and regulations regarding hiring practices. Moreover, a transparent AI process enables organizations to identify and mitigate biases, leading to fairer recruitment outcomes.

In contrast, implementing advanced machine learning algorithms may improve performance but does not inherently make the decision-making process more transparent. Similarly, increasing model complexity could obscure how decisions are made, creating further opacity rather than clarity. Relying solely on historical data can reinforce existing biases within the model and limits its ability to adapt to changing candidate profiles or market needs, ultimately compromising fairness and transparency.