Our Explainable AI Approach
At Interviewer.AI, we build Explainable AI  to help teams identify desirable talents
in their talent acquisition processes. Having an Explainable AI framework allows us
to provide insights on the key performance factors of candidates and minimize
the risk of prejudice judgements that may arise from black-box AI approaches.
Our Explainable AI assesses interview candidates’ soft skills through evaluation of
key success factors identified by I/O psychology heuristics and industry
knowledge , . From asynchronous video interview data provided by candidates,
visual, audio and textual information can be extracted for computer vision,
natural language processing, and audio analysis tasks for our AI to perform. These
observable pieces of information are evaluated by our Narrow AIs  that were
built to specifically measure these observable features of a candidate such as
eye-contact, emotional state, energy level, etc. These observable features are
then provided as data points to evaluate the candidates’ key success factors
through a combination of I/O psychology heuristics and machine learning to arrive
at the final interview score of the candidate.