7th May 2019

Evaluating assessments in the age of big data and AI

In association with:
Access now

Approaches to assessing job candidates and employees in talent management are experiencing rapid change. Much of the change is technologically driven by developments in machine learning and artificial intelligence (AI). For instance, automated video interview scoring, social media scraping, and gamification are relatively new methods deployed as assessments in talent acquisition.

These methods are fast, can be more engaging for candidates, and represent an important evolution in testing beyond 20th century approaches. But, however compelling these new methods, practitioners should not forget they are selection tests. They therefore need to meet stringent criteria related to group differences, bias, and standards for reliability and validity (e.g., evidence that they predict job performance or quality of hire).

In this paper, we give an overview of testing in talent acquisition and offer four specific guidelines for assessment practitioners evaluating new selection methods.

Download this Whitepaper

Share a few of your details to download your copy

First Name *
Last Name *
Job Title *
Company *
Phone Number *

You can withdraw your marketing consent at any time by sending an email to mailto:[email protected]. Also you may unsubscribe from receiving marketing emails from IBM by clicking the unsubscribe link in each such email.

More information on IBM processing of your personal data can be found in the IBM Privacy Statement. By submitting this form, I acknowledge that I have read and understand the IBM Privacy Statement.


By submitting this form you agree to our terms and conditions