Our accuracy is measured at the Observable Features level. On average, our models' test dataset accuracies are between 85-90% accurate. All models must have at least 70% test dataset accuracy to be considered for deployment.

We avoid the black-box approach where an unexplainable black-box AI is used to make judgements which could be judging candidates on an unfair basis. Instead, we approach the task from an Explainable AI approach where Narrow AIs are built to evaluate defined observable features with high accuracy, which are then used as inputs to calculate the key success factor scores using explainable machine learning methods where input feature importance can be extracted.

We are not aiming to replace the current traditional recruitment process performed by human HR professionals. Instead, we aim to augment that process by integrating the scalability and objectivity of Explainable AI interview assessment to allow the HR recruitment process to increase its effectiveness and efficiency.

Yes, our dataset is global. Our models are trained across different demographics and regions. Our AI is trained to be objective and not biased to any skin color, age or gender. We curate our datasets to avoid any historic data biases and validate our model on current data. As such, our AI is trained to be blind to gender, ethnicity and age.

This challenge occurs both with AI interviews and traditional recruitment interviews by humans if the candidate is speaking in a non-native language. One advantage our AI has is that it can assess interviews in different languages, which allows the candidate to present in their native language if the human interviewer allows multi-lingual responses on our platform.