What Is CognitIOn?
CognitIOn by Modern HireTM is the nucleus of everything we do today and foundational to how we’re transforming hiring tomorrow. It drives our research, innovation, and the scientists behind it who are committed to creating new and practical ways of measuring and understanding human performance.
Built on the discipline and principles of industrial-organizational psychology (I-O), CognitIOn is powered by tens of millions of candidate interactions, practical application of advanced artificial intelligence (AI) techniques, and almost two decades of diligent, data-driven client research and practice. More than AI promises and data points, CognitIOn is the deepest and broadest talent intelligence available for hiring the most diverse, engaged, and qualified workforce.
We are the top, trusted market resource on the latest breakthroughs in science, ethical and applied AI, and business ideas around modern hiring and the candidate experience.
There’s I-O inside everything we do.
"CognitIOn captures the unique way that Modern Hire leads with candidate transparency, combatting bias, and a foundation of theoretical science. It is refreshing evidence of how they are approaching hiring in a different way than other vendors."
Ben Eubanks, Principal Analyst, Lighthouse Research & Advisory
Modern Hire is one of the largest employers of PhD-level selection scientists dedicated to talent analytics in the world. Our more than 40 I-O and data scientists are the living core of CognitIOn, leading the industry in the investigation and use of advanced applied AI technology. They ensure the integrity of everything we do and that our approach is fair and predictive.
Many of our founders and members of our executive team are selection scientists, meaning the ethics and validity behind the science that drives our innovation will always be paramount. We focus on research in key areas of talent selection, behavioral science, culture and fit, fairness and ethical AI, the candidate experience, interviewing, and the workforce of the future.
Our scientists are thought leaders who are routinely engaged in research, publishing, speaking, and otherwise contributing to the field and understanding of talent acquisition.
How CognitIOn Is Different
What distinguishes CognitIOn is the practical application of deep learning and other advanced statistical techniques to create a system of intelligence, a fundamental understanding of how human capital drives organizational performance.
Deeper and broader than what underlies any existing HR technology, CognitIOn combines comprehensive expertise in the theoretical understanding of what drives human behavior, talent selection science, candidate experience, diversity and fairness, data science and advanced analytics, the practical application of ethical AI, and employment law.
Other tools might be built on one or even a handful of these disciplines, but only CognitIOn combines them all to create hiring tools that are optimally predictive, engaging, and fair.
We Use Glass-Box Science™
We promote transparency in the application of AI technology to talent acquisition and recognize its potential to both harm and help. Our firm position is that no instrument or technology should be applied to the hiring process without being rigorously vetted.
There’s a growing distrust of AI in the market because the technology is often used in a black box, where users can’t see or understand how decisions are being made. Modern Hire differs by taking an ethical Glass-Box Science approach that provides visibility into how data is collected and used.
CognitIOn is built upon exacting methodology and thorough documentation, ensuring fairness and defensibility. We evaluate only information that candidates knowingly provide, disclose to candidates how their data is evaluated and used, and do not engage in practices known to be unreliable, potentially unfair, or invasive, like using AI to evaluate facial features or scraping social media profiles.
Our Standards for Ethical AI in Hiring
AI products must operate transparently.
- We must ensure that substantively relevant data drives employment decisions and know what and how those factors are being evaluated and used.
AI product claims must be verifiable.
- Claims about the results of any AI-based product should be backed up by explanations of how data is collected and analyzed and what it successfully predicts.
AI research should be reproducible.
- AI developers should describe their methods in a forthright and open manner and seek to share and publish their findings whenever possible.
CognitIOn Enables Superior Outcomes
Forward-thinking science and technology mean better results for our clients. CognitIOn empowers organizations to rethink hiring and its bottom-line impact.
We have collaborated with clients to win an unprecedented four Human Resources Management Impact Awards from SIOP and SHRM in the last five years. The annual award recognizes the most successful HR practices and initiatives, as decided by evidence-based, data-driven analyses.
We partnered with Walmart, Amazon, and Bank of America to create and scale high-performing selection tools that:
- Support hiring for extremely high-volume and high-impact roles
- Predict performance and turnover, saving millions annually in new-hire retention
- Deliver an innovative and engaging simulation-based, job-relevant candidate experience
CognitIOn is embedded in award-winning hiring processes for leading global brands that have led to new measurements, more advanced applications of AI, millions of dollars in savings, and reinvention of the candidate experience.
“Modern Hire has created something special with CognitIOn and is telling a very differentiated story.”
Madeline Laurano, Aptitude Research
2020 Machine Learning Competition
Modern Hire data scientists are once again hosting the third annual SIOP Machine Learning Competition. The purpose of the competition is to develop new AI analytical methods for use in I-O psychology and HR analytics. This year teams are encouraged to compete in the creation and tuning of AI systems that assure fairness and utility in hiring. Last year an estimated 450 people from various industries and academia participated. Top methods and data are preserved and shared publicly to encourage others to reproduce and build upon the competition's resulting research. Registration remains open for 2020.
Phase III study: Stereotypes and hiring differences in veteran vs. non-veteran applicants
Each year, hundreds of thousands of servicepeople leave their military careers and make the transition back to civilian life. However, transitioning from military to civilian settings can be difficult. Our earlier research established that veterans consistently outperform non-veterans on certain pre-hire assessments, yet they are sometimes still not hired for roles for which they are likely to be successful. This project aimed to understand this hiring discrepancy. Read the full summary to find out how positive and negative stereotypes about veterans may influence the rate at which they are hired.
AI consent language research
We are studying how the AI disclosures and consent statements in our products inform trust and adoption of AI scoring. This research will help us better understand what’s required for candidates to feel comfortable being scored by AI and public attitudes about AI in a selection context. This study will also inform a larger research effort to understand candidate and recruiter reactions to using AI as part of interview and assessment scoring, as not much published research on the topic of candidate reactions to AI exists. Our research will contribute to the field and show how to implement these new approaches fairly and ethically.
Multiple outcome optimizer
We are at work on a multiple outcome optimizer that maximizes selection of top-performing job seekers while ensuring fairness in assessment-based algorithmic hiring. This tool drastically increases the accuracy, predictive power, and fairness of assessment, while reducing the time required to pinpoint the optimal scoring algorithm. Our scientific approach is consistent with selection best practices and is a first step in defining a pragmatic application of multi-objective optimization for algorithmic hiring by combining expertise in I-O psychology, mathematics, and computer science.
We are ensuring our models are well documented and that our clients have line of sight to our methods and can understand how our models are working. We want thorough documentation of how our models were built, how well they predict performance, and their fairness. We believe this transparency is essential to using AI in an ethical way.
Platform user insights
We are collecting and analyzing data to better understand how candidates are interacting with our interview technology and overall platform. This ongoing analysis will better equip us to answer client questions, prioritize new features, and provide insights for further research and thought leadership opportunities.
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