Definition
Balancing between false negative and false positive errors is the process of calibrating classification or detection systems to manage the trade-off between two types of errors:
- False positive (FP) - incorrectly identifying an element as positive (e.g. masking a region with no personal data).
- False negative (FN) - failing to identify a true positive element (e.g. missing a face that should be anonymized).
In visual data anonymization, this balancing aims to minimize privacy risk while maintaining high utility and data quality.
Importance in anonymization processes
In AI-supported anonymization systems:
- False negatives pose legal and ethical risks - potential data breaches and GDPR violations.
- False positives degrade media quality - unnecessary blurring reduces usefulness and interpretability.
Proper balancing supports compliance with the principles of data minimization and proportionality.
Balancing methods
Method | Description | Use case |
Threshold tuning | Adjust detection confidence thresholds | Lower threshold to reduce FN in face blurring |
Balanced metrics | Use F1-score, balanced accuracy, MCC | F1-score balances precision and recall |
Cross-validation / A/B testing | Evaluate multiple model configurations | Optimize blur accuracy in test environments |
Model ensembling | Combine outputs of multiple models | Reduce FN without increasing FP |
Rule-based postprocessing | Add deterministic logic to AI output | Catch faces missed by neural model |
Risk-based error prioritization | Choose lesser-risk error based on context | In livestreams: FP preferred over FN |
Consequences of poor balancing
Error type | Risk level | Possible consequences |
False negative | High | Privacy breach, GDPR penalty, reputational damage |
False positive | Medium | Over-masking, reduced utility, loss of content quality |
Additional consequences may include:
- Inadmissibility of visual evidence.
- Misinterpretation in training, teaching, or operations.
- Increased cost due to manual reprocessing.
Example use cases
- City surveillance face blurring systems - adaptive thresholding based on lighting and crowd density.
- Livestream anonymization - error calibration to prevent any face exposure.
- Ground truth dataset training - error logging and annotation to refine AI behavior.
- Hybrid validation pipelines - combining AI output with manual review for compliance.
Normative and technical references
- GDPR (EU 2016/679) - Articles 25 and 32 (privacy by design, processing security).
- ISO/IEC 22989:2022 - Artificial intelligence - concepts and terminology.
- ISO/IEC 24029-1:2021 - Robustness assessment of neural networks.
- EDPB Guidelines 3/2019 - On video surveillance and data protection.