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What inspires us

Academic Research

Our data science team is always learning and experimenting which means they are closely connected to other research in this area around the world.

Personality testing in employment settings: Problems and issues in the application of typical selection practices

Personality testing in employment settings: Problems and issues in the application of typical selection practices

Why we love it

This paper just nails everything that is wrong with traditional psychometric testing for hiring.It is great to read a visionary paper published in 2001 that foresaw all these issues almost 20 years earlier.

What I learnt from it

Some of the issues highlighted are,

  1. the (in)appropriateness of linear selection models;
  2. the problem of personality-related self-selection effects;
  3. the multi-dimensionality of personality;
  4. bias associated with social desirability, impression management, and faking in top-down selection models; and
  5. the legal implications of personality assessment in employment contexts.

Why it’s a must

Before fixing the issues of traditional psychometric testing for hiring, is it important to understand and acknowledge them. It also feels really good to see how PH’s approach solves some of these issues. We use machine learning models that are able to model complex non-linear relationships. Moreover, complex multi-dimensional relationships between the hiring outcome and not just personality, but rather a broad range of signal variables are LEARNT by our models. We use a chat-like text-based conversation to assess candidates and have noticed how hard it is to game such a system compared to MCQs used in psychometric testing. With regard to bias in our models, we constantly evaluate our models for gender and ethnicity bias and remediate such biases.

Who should pay attention

CHROs, Hiring managers, Talent Acquisition teams

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