Mitigating Age Biases in Resume Screening AI Models
نویسندگان
چکیده
As populations age, an increasing number of workers beyond the traditional retirement age are opting to continue working. Nevertheless, discrimination against older job seekers seeking new employment opportunities remains widespread. To address this issue, we enlisted a pool crowdworkers assess resumes IT candidates and guess each candidate's race, gender. Using crowdsourced data, trained AI model applied bias correction techniques from IBM's 360 Microsoft's Fairlearn toolkits correct for biases based on gender, age. We analyzed effectiveness these tools in mitigating different types hiring algorithms, explored why may be more challenging eliminate than other forms bias, discussed additional approaches enhance fairness. Our results indicate that implicit or ageism, is prevalent decisions pervasive well-documented such as race gender biases.
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ژورنال
عنوان ژورنال: Proceedings of the ... International Florida Artificial Intelligence Research Society Conference
سال: 2023
ISSN: ['2334-0762', '2334-0754']
DOI: https://doi.org/10.32473/flairs.36.133236