Study of accessibility and equity

Biases in hiring decisions continue to be an issue in the workplace. Even though hiring algorithms can provide advantages, they can still be widely regarded as tools that amplify human prejudices.Through a series of studies, we show that it is possible to de-bias hiring decisions through the construction and use of algorithms and data that are blind to gender, age, disability or ethnicity.Algorithms that are based on some of the most predictive indicators of an employee’s performance (Schmitt, 2014), such as a candidate’s personality and interests - on which people are also mostly similar (Hyde, 2005), are both effective and fair.Focussing on key factors of success that are blind to demographics, AssessFirst’s assessments of SHAPE, DRIVE and BRAIN make it possible to boost your diversity.

• • Our assessment results are proved to be blind regarding gender, age, ethnicity, disability and a range of neurodiverse conditions
  • Our scoring algorithms are non-discriminatory and equitable: the profiles that will be recommended by the algorithm to hiring managers present demographic characteristics that are similar to those of the entire pool of candidates applying to the position

• Our solution can naturally correct gender-imbalance in your organisation; even when training the algorithm on a male-dominated sample, the algorithm will go on to recommend men and women in almost equal proportions when applied on a neutral sample.

This document makes the synthesis of these studies. Also, it provides general information about the efforts we deploy concerning diversity, and what make AssessFirst an essential player when it comes to user experience and inclusivity.