Webif the underlying data is unfair, the resulting algorithms can perpetuate bias, incompleteness, or discrimination, creating potential for widespread inequality. Webyears ago, linkedin discovered that the recommendation algorithms it uses to match job candidates with opportunities were producing biased results. Webalgorithms have been found to automatically assign job candidates different scores based on arbitrary criteria like whether they wear glasses or a headscarf. Web“to a job seeker and a recruiter, the ai is a little bit of a black box,” says hilke schellmann, whose book the algorithm looks at software that automates résumé. Webthese tools are not eliminating human bias — they are merely laundering it through software. Algorithms that disproportionately weed out job candidates of a. Rather, it is the start of a journey to ensure that ai lives up to its potential. Box 1 defining bias and fairness bias and fairness are complex human notions. Webunderstanding bias in hiring algorithms and ways to mitigate it requires us to explore how predictive technologies work at each step of the hiring process.
