Using LLMs to evaluate technical interview performance (dokasto.com)

A new approach to using Large Language Models (LLMs) for evaluating technical interview performance is gaining traction, raising both excitement and concern within the AI/ML community. As interviewers increasingly rely on LLMs to generate feedback from transcripts, potential pitfalls like biased outcomes and hallucinated details emerge. The ease and speed of AI-generated evaluations can obscure critical errors, leading to substantial hiring decisions made with minimal oversight. A proposed solution emphasizes redesigning workflows to prioritize structured interviews and behaviorally anchored rubrics, ensuring findings are auditable and justifiable. The suggested framework introduces a more rigorous methodology for integrating LLMs into hiring processes. It encourages the use of a structured interview format coupled with behaviorally anchored rating scales to provide clear examples of desired candidate behaviors. Instead of letting LLMs make judgments independently, the revised workflow involves extracting grounded evidence from transcripts, linking evaluations to concrete segments for human verification. By addressing these challenges, such as verbosity bias and reliance on flawed transcripts, the approach aims to make LLM-assisted hiring robust and defensible, ultimately enhancing hiring quality while mitigating risks associated with automated evaluations.