Over 200 teams competed, submitting over 1,500 algorithms to be ranked on a large open source data set, to produce the most fair and valid algorithms for pre-employment screening
Cleveland and Delafield, Wisc. (June 15, 2021) –– Modern Hire, a science-based hiring platform that enables organizations to continuously improve hiring experiences and outcomes with trusted science and technology, today revealed the winners of the third annual Society for Industrial and Organizational Psychology (SIOP) Machine Learning Competition, along with the data and the code for the winning solutions.
Engineered and designed by Nick Koenig, Ph.D. and Isaac Thompson, Ph.D., Principal Data Scientists at Modern Hire, the SIOP Machine Learning Competition enables participants to transform hiring for the better. Exclusively sponsored by Modern Hire since its inception, the goal of this year’s competition was to leverage the latest innovations in artificial intelligence to develop an algorithm that predicts employee performance and encourages diversity of qualified candidates. Using a massive open data set from a Fortune 100 company, participants were given the real-world challenge that is extremely critical in today’s hiring environment.
“Getting access to such a rich real-world data set of pre-employment and post-employment data set a strong foundation for this year’s competition. We are not only open-sourcing the data, which creates an organic and evolving benchmark for innovation of the most fair and valid methods, but also the winning teams’ solutions,” said Thompson. “The interesting results spur a novel yet informed conversation around approaches to fairness that have maybe not been considered or should be reconsidered.”
More than 200 teams from across industry and academia entered and submitted over 1,500 models to this year’s 2021 SIOP Machine Learning Competition. Approximately six hundred individuals participated, 60% for whom it was their first ever machine learning competition.
The winners of the 2021 competition, which were announced during the SIOP Annual Conference in April, are:
- First place: Feng Guo and Samuel T. McAbee from Bowling Green State University.
- Second place: The Axiom Consulting Partners team, composed of Ian Burke and Ashlyn Lowe from Axios Consulting Partners, Goran Kuljanin from DePaul University and Robin Burke from the University of Colorado, Boulder.
- Third place: Brain Costello and Willy Hardy from Red Hat.
- Fourth place: The Colorado State University team, composed of Joshua Prasad, Steven Raymer, Kelly Cave and Shayln Stevens of Colorado State University and Jason Grant Prasad from the Georgia Institute of Technology.
The competition was hosted on EvalAI, an open-source AI challenge platform. Since all algorithms were written in open source and fully reproducible code, any company will be able to leverage the created algorithms to make more fair and accurate hiring decisions. All algorithms and data have been made available openly on GitHub and can be accessed here. Similarly, a recording of the presentation can be found on SIOP’s official YouTube channel.
For more information on Modern Hire’s award-winning, science-based enterprise hiring platform, please visit: https://modernhire.com/platform/.
About Modern Hire
Modern Hire is a science-based hiring platform that improves hiring decisions with sophisticated candidate screening, predictive assessments and interviewing technology. Nearly half of the Fortune 100 use Modern Hire, a technology that combines AI, predictive analytics, workflow automation, assessment and interviewing technology in a single solution that integrates with leading HCM systems. CognitIOn by Modern Hire™, the nucleus of the platform, merges expertise in industrial organizational psychology, talent selection science, advanced analytics, candidate experience, employment law, data science and the practical application of ethical AI. To learn more about how the company helps enterprise organizations meet the hiring challenges of today, visit www.modernhire.com.