Systematic Review Map of “Invisible” Information: Charting Detection Gaps Across Domains

Building on the “reorganize current knowledge” heuristic and systematic reviews like Zhao et al. (2023) and Fanlau et al. (2025), this project would synthesize not just where missing data is handled, but where it is systematically ignored or undetected—such as uncollected variables, unreported populations, or algorithmic blind spots. By developing a taxonomy of invisibility (e.g., “unknown unknowns,” structural missingness, silent minorities), and mapping available detection methods (statistical, ML, qualitative, checklist-based), this work would create a meta-level resource for researchers across fields. The innovation lies in making “absent information” itself a focus of systematic review, surfacing underexplored blind spots (like those critiqued in Yu et al., 2023) and guiding future tool development toward the most consequential gaps. This could catalyze new detection frameworks and ethical standards for data completeness.

References:

1. Effect of Missing Data Types and Imputation Methods on Supervised Classifiers: An Evaluation Study. Menna Ibrahim Gabr, Y. Helmy, D. Elzanfaly (2023). Big Data and Cognitive Computing.
2. What is Missing in Missing Data Handling? An Evaluation of Missingness in and Potential Remedies for Doctoral Dissertations and Subsequent Publications that Use NHANES Data. Hairui Yu, S. Perumean-Chaney, K. Kaiser (2023). Journal of Statistics and Data Science Education.
3. A Systematic Review of Individual Tree Crown Detection and Delineation with Convolutional Neural Networks (CNN). Haotian Zhao, J. Morgenroth, Grant D. Pearse, Jan Schindler (2023). Current Forestry Reports.
4. Approaches, Applications, and Challenges of Using Sentiment Analysis for False Information Detection: A Systematic Literature Review. Nathan Edmund Fanlau, Michael Hiu, Said Achmad, Rhio Sutoyo (2025). International Conference on Ubiquitous Information Management and Communication.