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Audition AI Recruiting Agents
This talk explores building modular AI agents that conduct dynamic, unbiased recruiting interviews using LLMs and cognitive architectures without relying on databases.
Interviews are critical because they determine the people you hire. They are, however, also messy, unstructured, filled with human bias. The interviewer’s mood determined by their emotional state, time of the day and even how hungry they are, unfairly affect the outcome for candidates. Additionally, measuring technical capability requires domain expertise.
This is a broken system, organizations are missing out on great talent, and candidates barely ever get feedback.
At Audition, we are building generalized AI agents to conduct interviews. We envision conducting all the interviews including technical, simulation and even panel interviews to evaluate the ability of the candidate. The human speaks to highly vetted candidates, where they can evaluate them for human qualities like empathy, likeability, optimisms, etc.
The key that makes this possible is:
- an LLM is the average of human domain knowledge. So using it as a judge allows you to evaluate human’s ability too.
- AI agent with cognitive architecture that allows them to conduct dynamic interviews, and respond on the basis of the user’s input. Thus the no two interviews are ever the same
For this demo I will be showing the first steps of this, where an agent is gathering the information to construct itself with a human’s help.