How Can One Tackle the AI Hiring Bias?

Businesses have found it easier and more time-efficient to fill job openings owing to the vast improvements in technology over the last few years. The expanding usage of artificial intelligence in particular, has also had a significant impact on efforts to enhance diversity within firms and industries. However, the bias that tags along with artificial intelligence is frequently unintentionally incorporated into algorithms and can undermine firms' genuine efforts to enhance diversity. Here are a few helpful tips to help eliminate or reduce bias in AI:

• Avoid relying solely on an Automated AI Engine for Hiring: Keep humans in the picture! Hiring teams, for example, can help devise a method for balancing the various kinds of resumes given into the AI engine and may even implement a manual review of recommendations to ensure that the results are just and fair.

• Check and Re-Check your Model Training: Proactively build and test the tools and data used to train your model and ensure that when adding a data point, you evaluate whether there is a propensity for that pattern to be more or less prominent in a protected class that may introduce a bias.

• Use Proven AI Techniques: Proven techniques can reassure enterprises that the AI model on which they rely will perform well. While some tweaking is required, established AI solutions can help organizations decrease prejudice while also making it more time and cost effective.

• Analyze your Outcomes: Once adopted, hiring tools should undergo frequent reviews under human supervision in order to ensure they match expectations and don’t falsely recommend unfit candidates for the given roles.

One famous example of AI bias in hiring is Amazon’s experience with an AI recruiting tool that turned out to be biased against women after being trained with 10 years of recruitment data, which accidentally taught the AI model that men had historically been preferred over women in tech professions. So for businesses that decide on using AI as a part of its hiring process, it is absolutely crucial to understand the different ways that bias may affect their results!