Pre-Hire Talent Assessment: Minimising Bias and Optimising Hiring Decisions

Saturday, July 1, 2023, 12:00 AM
(UTC+02:00) Berlin, Bern
Vanessa Lammers

This presentation will review the many places that bias can creep into the talent assessment and selection process, and ways in which organisations can change their processes to eradicate such bias.

Specific focus will be given to interviews and pre-hire assessments, and ways in which organisations can minimise bias and optimise hiring decisions.

Focus: Talent Acquisition; Diversity, Equity & Inclusion

  • The success of an organisation is closely tied to the quality of its workforce, which in turn is closely tied to the effectiveness of its talent acquisition process.
  • Part of optimising talent acquisition processes includes ensuring that our selection systems do not give an advantage to any particular subgroup, thus providing greater opportunities to increase diversity in the organisation.
  • Diversity and Inclusion are necessary components of an organisation’s health, and lead to many positive organisational outcomes – from increased creativity and innovation, to problem solving and decision making.
  • In order to realise these benefits of diversity, we must minimise bias in TA process
  • Organisations should take a critical look at their talent management systems and processes to identify and eradicate areas of bias and potential group differences and ensure the promotion of diversity.

This session will explore:

  • Common unconscious biases
  • The ways in which bias can impact various decision points in the talent acquisition process
  • Common problems and proposed solutions, with respect to interviews and pre-hire assessments specifically.

Learning outcomes:

  • Understanding of the components and benefits of a structured interview approach, and how to implement structured interviews at one’s own organisation
  • Important considerations regarding pre-hire assessments, including conducting job analyses to determine job requirements, demonstrating job relatedness and setting appropriate cut scores based on job requirements, and assessing and monitoring adverse impact before and after assessment go-live.