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How AI’s Bias Problem Can Affect Your Next Job Application
Manage episode 419839913 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/how-ais-bias-problem-can-affect-your-next-job-application.
AI bias in recruitment is a growing concern for tech job seekers. Despite its potential for automating HR tasks, AI can perpetuate biases.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-bias, #ai-bias-in-recruitment, #job-hunting, #tech-jobs, #ai-in-hiring, #deandi-in-recruitment, #hackernoon-job-board, #hackernoon-top-story, and more.
This story was written by: @amply. Learn more about this writer by checking @amply's about page, and for more stories, please visit hackernoon.com.
AI bias in recruitment arises when systems and tools that are used to screen and select candidates, inadvertently perpetuate or even amplify existing prejudices. These biases often stem from the data these systems are trained on, which can reflect societal prejudices or historical hiring patterns. Names distinct to Black Americans had a lower likelihood of being listed as the best applicant. Names connected to Asian women and white men generally performed better.
316 פרקים
Manage episode 419839913 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/how-ais-bias-problem-can-affect-your-next-job-application.
AI bias in recruitment is a growing concern for tech job seekers. Despite its potential for automating HR tasks, AI can perpetuate biases.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-bias, #ai-bias-in-recruitment, #job-hunting, #tech-jobs, #ai-in-hiring, #deandi-in-recruitment, #hackernoon-job-board, #hackernoon-top-story, and more.
This story was written by: @amply. Learn more about this writer by checking @amply's about page, and for more stories, please visit hackernoon.com.
AI bias in recruitment arises when systems and tools that are used to screen and select candidates, inadvertently perpetuate or even amplify existing prejudices. These biases often stem from the data these systems are trained on, which can reflect societal prejudices or historical hiring patterns. Names distinct to Black Americans had a lower likelihood of being listed as the best applicant. Names connected to Asian women and white men generally performed better.
316 פרקים
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