How an Ai for hiring can help

AI technologies are able to find and learn patterns in large and diverse datasets to extract insights as well as predict future outcomes. AI based solutions are becoming prevalent in many business domains including self driving cars, movie recommendations and whether someone is eligible for a loan or parole.

AI algorithms can be used to assess candidates to measure their likelihood of success in terms of performance in a given role and churn-risk. But is AI bias free? AI is designed to find existing patterns, not create new patterns; thus a garbage-in-garbage-out philosophy is of a vital nature. If the data used to train AI has inherent biases, then the outcome from such an AI model is going to produce biased results.

There are fundamental differences in Ai algorithms and human bias:

1. Ai bias is testable, hence fixable. Both training data and Ai models can be tested for bias. Appearance of any bias can lead to corrective actions or disposal of the model altogether.
2. Ai can be trained to ignore demographic information such as gender, race, and age. Implicit latent patterns (such as gender-specific terms in CVs) can also be detected with testing.
3. Continuous monitoring of candidate data, predictions and post-hire performance of employees, leads to better understanding of bias and further-enhanced predictive models.