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.
Our AI models are trained to be blind towards age, genders, ethnicity for a fair and objective assessment approach. Our model training datasets are global and purposefully curated to avoid any undesirable historical biases in hiring ,, . We ensure that there are fair representation of age-groups, genders, and ethnicity for each label in our supervised training datasets.
When augmented with traditional talent acquisition human resource processes, our Explainable AI brings the value of objectiveness, scalability, and explainability to the professional human teams to boost the effectiveness and efficiency of the department.
Addressing Common Concerns
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