Automated Interview Scoring Bias Detection and Mitigation
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Solution Overview
Problem
Existing computer-based hiring systems often perpetuate biases, leading to adverse impacts on candidates with disabilities due to human bias in the review process and inadequate data processing, which can result in discriminatory practices and a compromised workforce quality.
Innovation Solution
A model training tool that processes digital interview data to identify features correlating with disabilities, applies normalization processes to reduce adverse impacts, and uses machine-learning techniques like deep learning to mitigate biases, ensuring equal weighting of candidate classes and improving prediction accuracy without human bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human administrators handle the candidate review process, then flexibility and contextual understanding are improved, but human bias creeps in resulting in adverse impact on candidates with disabilities
Solution Approach 1:
The patent replaces the mechanical human review process with an automated computer-based system that processes candidate data without human intervention. This substitution eliminates human bias while maintaining the ability to evaluate candidates systematically, addressing the contradiction between operational flexibility and harmful bias.
Solution Approach 2:
The patent introduces an intermediary automated review system that sits between the candidate and the hiring decision. This intermediary processes candidate information through standardized algorithms, preventing direct human bias from affecting the evaluation while preserving the ability to make informed hiring decisions.
2Extent of automation
If computer models are employed to automate the review process, then human bias is reduced, but biases may be integrated or trained within the model perpetuating existing prejudices
Solution Approach 1:
The patent applies preliminary action by proactively identifying and correcting biases in the training data before the model is deployed. The system performs bias detection and mitigation during the model training phase, ensuring that the automated review process does not perpetuate existing prejudices against disabled candidates.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor the computer model's performance for biased outcomes. When bias is detected in the model's predictions, the system provides feedback to adjust and retrain the model, ensuring that automation does not lead to discriminatory practices.
3Device complexity
If existing computer models are used without revision or update, then operational simplicity is maintained, but the models keep perpetuating human bias making them less accurate and less unbiased
Solution Approach 1:
The patent applies dynamics by making the computer model adaptive and capable of continuous improvement. Rather than maintaining a static model, the system periodically retrains and updates the model with new data and bias mitigation techniques, balancing operational simplicity with ongoing accuracy and fairness improvements.
Data Source
AI summary
A processing device is to: identify, using digital interview data of interviewees captured during interviews, a subset of the interviewees that have a disability; label a first group of the interviewees as disabled and a second group of the interviewees as not disabled with reference to the disability; identify features from the digital interview data for the first group that correlate with the disability; formulate a digital fingerprint of the features that identifies how the first group differs from the second group with reference to the disability; map the digital fingerprint of the features onto a dataset of an interviewee belonging to the second group of the interviewees, to generate a mapped dataset; and determine, from the mapped dataset, effects of the digital fingerprint on a job performance score for the interviewee.