CHRO/HRD asks: Why don't we share data on specific individuals?

TL;DR:

  • Risk of bias and misinterpretation
  • Psychometric expertise required
  • Essentialization and labeling harm people
  • Sharing can cause damage
  • Companies get effects and recommendations instead of raw numbers

Why does Empatyzer not disclose identifiable employee data?

Empatyzer translates knowledge about others into practical tips for everyday collaboration, rather than providing users with someone else's full diagnoses and raw results.

Features that can help:

  • Tips: Provides functional tips for working with a specific person, focused on what can help the relationship.
  • Comparison: Shows the differences between two people in a relational form, so the user understands the mechanism without having access to someone else's full profile.

Raw individual results are not shared because, on their own, they offer little value for decision-making without professional interpretation. Correct interpretation requires psychometric skills and contextual knowledge; without them, misunderstandings are likely. Sending single-person profiles creates a high risk of bias, oversimplification and essentialization: labeling people by a few scores reinforces prejudices and can harm careers and team relationships. Empatyzer provides a consolidated effect and actionable recommendations rather than raw figures. The tool automatically processes data in a safe, nonjudgmental way and generates advice tailored to relationships and context. Publishing raw results also increases the risk of improper use in performance reviews, recruitment or internal politics. Individual-level data can be misread even by experienced psychologists, so we supply companies with aggregates and trends. Aggregates help identify cultural risks, training topics and areas for intervention without exposing individual privacy. Conversation content is protected: outputs are not shared with managers or HR, and accounts and data can be deleted or withdrawn. This policy builds trust and adoption, which are necessary for the tool to work effectively at the organizational level. In short: we protect people from harm and give companies useful effects instead of raw, easily abused numbers.

We do not share individual data to prevent bias, protect privacy and deliver practical effects instead of raw results.

Sources and research

  1. American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for Educational and Psychological Testing. American Educational Research Association. Source
  2. Amos Tversky; Daniel Kahneman (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124 DOI: 10.1126/science.185.4157.1124
  3. William M. Grove; David H. Zald; Boyd S. Lebow; Beth E. Snitz; Chad Nelson (2000). Clinical versus mechanical prediction: A meta-analysis. Psychological Assessment, 12(1), 19–30. https://doi.org/10.1037/1040-3590.12.1.19 DOI: 10.1037/1040-3590.12.1.19
  4. DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93(5), 880–896. https://doi.org/10.1037/0022-3514.93.5.880 DOI: 10.1037/0022-3514.93.5.880

Author: Empatyzer

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