Is data minimisation possible?

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Non-compliance CategoryTechnique & Processes Category
Design PhaseInput PhaseModel PhaseOutput Phase
Is data minimisation possible?

Although it appears to contradict the principle of data minimisation, not using enough data could sometimes have an impact in the accuracy and performance of the model. A low level of accuracy of the AI system could result in critical, adversarial or damaging consequences. Can you still comply with the data minimisation principle?

If you answered No then you are at risk

If you are not sure, then you might be at risk too

Recommendations

  • Sometimes data minimisation can be achieved by using less features and training data that is of good quality. However it is not always possible to predict which data elements are relevant to the objective of the system.
  • Consider to start training the model with less data, observe the learning curve and add more data if necessary, thereby justifying why it was necessary.
  • The usage of a large amount of data could be compensated by using pseudonymisation techniques, or techniques like perturbation, differential privacy in pre-processing, use of synthetic data and federated learning.
  • Try to select the right amount of features with the help of experts to avoid Curse of dimensionality (which means that errors increase with an increase in the number of features)

Interesting resources/references