New Talent Signals: Shiny New Objects or a Brave New World?
Citation. Chamorro-Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R. (2016). New talent signals: Shiny new objects or a brave new world? Industrial and Organizational Psychology, 9(3), 621-640. https://doi.org/10.1017/iop.2016.6
Summary. Chamorro-Premuzic and colleagues review emerging digital talent-identification tools, including smartphone profiling, social media, games, big data, and other technology-mediated signals. Their core argument is not anti-innovation; it is that I-O psychology should evaluate new talent signals using the same validity, construct, and utility standards applied to traditional assessment. The paper is a strong bridge source for this cluster because it frames algorithmic assessment as a measurement-design problem before it is a software or automation problem.
Key claims
- Technology was already disrupting talent identification by 2016, producing tools for quick and inexpensive inference about human potential and future work performance.
- The authors argue that academic I-O psychologists had largely been spectators, leaving HR practitioners with limited credible evidence for evaluating innovative assessment methods.
- New assessment tools are often technologically enhanced versions of traditional methods, so traditional workplace assessment categories can organize their evaluation.
- Established I-O evidence supports the validity of job interviews, assessment centers, cognitive ability tests, personality inventories, biodata, situational judgment tests, 360-degree feedback, resumes, letters of recommendation, and supervisor ratings; new tools should be compared against this existing evidence base.
- The authors warn that many new tools had not demonstrated validity comparable with older methods, tended to disregard theory, and paid too little attention to the constructs being assessed.
- They emphasize that prediction matters, but scientifically defensible assessment also requires understanding behavior and explaining what constructs the tool measures.
Evidence & limitations
- This is a focal review/conceptual article rather than a new empirical validation study.
- The article predates current generative AI and modern regulatory debates, so it should be used for foundational framing rather than current technical claims.
- The piece is especially useful for construct validity and evidence-based management because it names the failure mode: novelty and proprietary prediction can outpace theory, transparency, and validation.
- Some examples and technology categories are time-bound, but the measurement standards remain directly relevant to algorithmic hiring and AI-mediated assessment.