What action should a company take to improve the transparency and accountability of a generative AI system used for evaluating job applications?

Establishing and enforcing policies that support explainability in generative AI systems is crucial for improving transparency and accountability. This approach ensures that the organization has a clear framework guiding the development and operation of its AI systems, particularly in sensitive areas like job application evaluations.

By prioritizing explainability, the company can develop models that delineate their decision-making processes. This can involve clarifying how data inputs lead to certain outputs, allowing stakeholders—including applicants—to understand how decisions are made. In turn, this promotes trust among candidates and helps the company mitigate risks associated with bias or discrimination in AI-driven evaluations.

Moreover, establishing such policies empowers the organization to regularly review and update practices related to generative AI, ensuring they evolve alongside advances in technology and changes in ethical standards. This sustained commitment to explainability can significantly boost the overall integrity of the hiring process.

While audits, manual reviews, and automated explanations are also important aspects of managing AI systems, they may not address the foundational need for clear guidelines and principles that govern how those systems function. Respectively, they might assist in specific tasks but do not ensure the same level of systemic transparency and accountability offered by robust explainability policies.