Knockri’s A.I. is unbiased because of its full spectrum database that ensures there’s no benchmark of what the “ideal candidate” looks like. However, what other steps are taken to ensure that the tool’s decisions are accurate and bias free?
Read below to find out what CTO Faisal Ahmed, has to say about how the team ensures we give companies results with optimal accuracy!
There are many steps the Knockri team takes to ensure that companies hire the right candidate and base their decisions on both accurate and unbiased data. Beyond the dataset, there are a lot of ways we can avoid the pitfalls of our own unconscious bias. For this, it’s important to have a holistic mind frame when approaching this issue and implement systems that prevent human errors from happening.
With that said, a human touch is essential for quality control to identify whether any biases are being developed when introducing new data to the A.I.
We can also ensure unbiased AI decision-making by implementing algorithmic processes in the decision-making. These algorithmic processes look into the results of your AI’s predictions and test whether the decisions are biased. If they are, we can retroactively go back and fix those biases in our own datasets and re-train our AI algorithms.
So since the A.I. is only as good as the data is, we do everything we can to make sure our dataset is the most inclusive and diverse, so no candidate slips through the cracks!