Organizations are increasingly using modern technology to collect diverse, high-volume and real-time workforce data. They apply AI and machine learning algorithms to these data, hoping to reap the benefits of improved employee productivity and well-being at work. Within this context, the collection and analysis of multi-channel psychological data relevant to the workforce has been widespread, e.g., attitudes, decisions, behaviors, team interactions, response latencies and physiological measures.

However, just because we now have vast and powerful technologies for data collection and analysis does not mean that we will necessarily obtain better workforce solutions in the end. Scientifically sound workforce solutions require high-quality data, measurement and analysis (not simply big data), and interpretable and informative results (not simply prediction for its own sake).

The current workshop convenes renowned experts from multiple disciplines that address measurement, modeling and interpretation in the big data and workforce context.

The workshop will address the following objectives:

• Understand effective substantive and statistical approaches to extract psychological constructs from big data relevant to the workforce
• Appreciate the traditional concepts and hard-won lessons of reliability, validity and fairness as they apply today’s world of modern workforce measurement and analysis
• Identify pathways to improving the substantive interpretability of predictions from big data and machine learning (vs. “black box” predictions)
• Compare big data and machine learning to more traditional workforce approaches in terms of yielding practical improvements in prediction, interpretation and decision making
• Explore the nature and benefits of dynamic models and methods to capture important changes and interventions related to employees, teams and workforce management in real-time data flow
• Appreciate interacting multilevel workforce data structures that apply to issues of measurement analysis and interpretation (e.g., employee adaptability over time, individuals interacting within teams, interacting teams over time)
• Identify and discuss ethical and legal concerns pertaining to job applicant and employee data collected from technology- and algorithm-driven technologies
• Foster interdisciplinary collaboration and communities that will improve how the basic research community addresses substantive, statistical, legal and ethical issues relevant to workforce science

This research activity is supported by the U.S. Army Research Institute for the Behavioral and Social Sciences (ARI) under Grant Number W911NF-19-1-0314. The views, opinions and/or findings contained in this workshop are those of the authors and shall not be construed as an official Department of the Army position, policy or decision, unless so designated by other documents.