Implementing recruitment assessment models directly on candidate devices reduces the need to send personal information to cloud servers. On-device ML supports privacy preservation and inclusivity by enabling real-time, personalized evaluation with minimal exposure of sensitive data, which is particularly important for marginalized communities wary of data misuse.

Implementing recruitment assessment models directly on candidate devices reduces the need to send personal information to cloud servers. On-device ML supports privacy preservation and inclusivity by enabling real-time, personalized evaluation with minimal exposure of sensitive data, which is particularly important for marginalized communities wary of data misuse.

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