How should a company address the lack of transparency in its generative AI model that screens job applications?

Focusing on fine-tuning the generative AI model for better explainability directly addresses the core issue of transparency. In the context of screening job applications, stakeholders—including applicants and hiring managers—benefit from an understanding of how decisions are made. Fine-tuning the model involves adjusting its parameters, algorithms, and possibly the data it is trained on to provide clearer insights into the rationale behind its outputs. This can include implementing techniques such as incorporating feature importance scores or using interpretable model architectures that allow users to follow the decision-making process.

Enhancing explainability helps to build trust among users and can alleviate concerns about bias, discrimination, or unjust decision-making, which is particularly important in hiring practices. By ensuring that the AI model provides understandable and interpretable results, the company demonstrates a commitment to fairness and accountability. This approach ultimately fosters a more positive perception of the AI system among all parties involved.