Study of the influence of a biased database on the prediction of standard algorithms for selecting the best candidate for an interview
This addresses fairness issues in AI-driven recruitment for companies, but it is incremental as it builds on existing bias research.
The study investigated how biased training data affects the performance of standard algorithms in selecting the best job candidates, finding that biases like discrimination and self-censorship reduce prediction quality, with anonymization partially mitigating this.
Artificial intelligence is used at various stages of the recruitment process to automatically select the best candidate for a position, with companies guaranteeing unbiased recruitment. However, the algorithms used are either trained by humans or are based on learning from past experiences that were biased. In this article, we propose to generate data mimicking external (discrimination) and internal biases (self-censorship) in order to train five classic algorithms and to study the extent to which they do or do not find the best candidates according to objective criteria. In addition, we study the influence of the anonymisation of files on the quality of predictions.