It’s hard to believe but SIOP’s 2019 LEC is over and the planning for SIOP 2020 LEC is underway! I’d like to take a moment to reflect on the annual consortium and start translating the amazing insights and discussions had during LEC into action.

SIOP’s 2019 Leading Edge Consortium, Advancing the Edge: Assessment for the 2020s (LEC), had record-breaking attendance. In fact, it SOLD OUT! What was even more exciting was that about 20% of the audience were non-SIOP members. Their unique perspectives and expertise greatly contributed to and heightened the conversation about where the field of assessment needs to head in the future.

Brendan Neuman, PhD - Basil Consultants and Maura Burke, PhD - HumRRO

With increasing coverageartificial intelligence of automated talent assessment appearing in the popular press, it’s no surprise that employers’ expectations are moving toward automated selection processes. That said, there is a limit to what can be effectively and responsibly automated, as the interaction between human behavior and workplace context is messy and difficult to quantify. Although AI and automation can be pragmatic aids to recruit talent, selection decisions are more ambiguous, involve higher stakes, and require a great deal of contextual information. In this article, we explore the state of automation in talent assessment and shed light on its benefits and limitations.

SIOP understands that finances can be tight for Student Affiliates and wanted to help by offering an early bird renewal drawing, exclusively for students.

Roughly 1,000 students were entered in our drawing, simply for renewing their dues by June 15. SIOP is happy to announce the three lucky students that won free registration for the 35^th Annual SIOP Conference.

Algorithms are increasingly used in assessment methods and technologies. The word algorithm is used colloquially in different ways, but in this context it generally refers to computer programs that utilize complex statistical models to combine tens, hundreds, or even thousands of variables to predict an outcome (e.g. job performance) or automate a process (e.g. eliminate unqualified applicants).

But although algorithms clearly have the potential to increase our ability to make better decisions, industrial-organizational (I-O) psychologists and those in related fields have yet to fully consider how to optimize the collaboration between human decision makers and algorithmic decision aids. Viewing the human–algorithm relationship as a collaboration is fitting because better decision-making outcomes are possible when both are involved rather than when making decisions separately.