4 ways AI is contributing to bias in the workplace

Generative AI tools are often used to screen and rank candidates, create resumes and cover letters, and summarize several files simultaneously. But AIs are only as good as the data they’re trained on. 

GPT-3.5 was trained on massive amounts of widely available information online, including books, articles, and social media. Access to this online data will inevitably reflect societal inequities and historical biases, as shown in the training data, which the AI bot inherits and replicates to some degree. 

Also: Five ways to use AI responsibly

No one using AI should assume these tools are inherently objective because they’re trained on large amounts of data from different sources. While generative AI bots can be useful, we should not underestimate the risk of bias in an automated hiring process — and that reality is crucial for recruiters, HR professionals, and managers.

Another study found racial bias is present in facial-recognition technologies that show lower accuracy rates for dark-skinned individuals. Something as simple as data for demographic distributions in ZIP codes being used to train AI models, for example, can result in decisions that disproportionately affect people from certain racial backgrounds.

Generative AI tools are often used to screen and rank candidates, create resumes and cover letters, and summarize several files simultaneously. But AIs are only as good as the data they’re trained on. 

GPT-3.5 was trained on massive amounts of widely available information online, including books, articles, and social media. Access to this online data will inevitably reflect societal inequities and historical biases, as shown in the training data, which the AI bot inherits and replicates to some degree. 

Also: Five ways to use AI responsibly

No one using AI should assume these tools are inherently objective because they’re trained on large amounts of data from different sources. While generative AI bots can be useful, we should not underestimate the risk of bias in an automated hiring process — and that reality is crucial for recruiters, HR professionals, and managers.

Another study found racial bias is present in facial-recognition technologies that show lower accuracy rates for dark-skinned individuals. Something as simple as data for demographic distributions in ZIP codes being used to train AI models, for example, can result in decisions that disproportionately affect people from certain racial backgrounds.