HumenhukAIGP Study Guide
Module 6: Governing AI Development, BoK III.B

Wrangling the Data

Reviewed by Victor Humenhuk. Published . Reviewed .

Five considerations turn raw data into model-ready data without trampling privacy: cleansing, labelling, anonymisation and minimisation. Master the two PETs - differential privacy blurs the data; federated learning moves the training, not the data.

Five considerations turn raw data into model-ready data without trampling privacy.

  • Data cleansing → removing erroneous and irrelevant data → protects performance and reliability, and cuts privacy risk by stripping unnecessary personal data → note that complete anonymisation is difficult because combining datasets can reidentify individuals
  • Data labelling → tagging or annotating data → categorising images, transcribing audio, tagging text → accuracy directly affects learning → keep labelling consistent, by trained personnel, with collaborative tools for quality control
  • Anonymisation → removing items that could identify individuals → names, addresses
  • Purpose specification & minimisation → data unnecessary for the application should not train the model → minimising personal data protects privacy

The two PETs to know in depth:

PET 1, Differential privacy

Blurs information within datasets using advanced algorithms → data stays meaningful for analysis but becomes nonspecific enough to prevent identifying individuals → valuable in healthcare and finance.

PET 2, Federated learning

Local systems train a central model independently on their own datasets → results are aggregated centrally (e.g., the cloud) without exposing individual data → iterates until the global model is trained → suits diagnosing illnesses across multiple locations.

Exam flash

Definition pair to keep straight → Differential privacy blurs the data, Federated learning moves the training, not the data. If the scenario says "data never leaves the local site" → federated learning.

Key terms - quick answers

What is “Data cleansing”?
Removing erroneous and irrelevant data to protect performance and reduce privacy risk.
What is “Data labelling”?
Tagging or annotating data (images, audio, text); accuracy directly affects learning.
What is “Anonymisation”?
Removing items that could identify individuals, such as names and addresses; complete anonymisation is difficult.
What is “Purpose specification & minimisation”?
Keeping data unnecessary for the application out of model training to protect privacy.

Sources and study method

This independent lesson uses active recall, spaced retrieval and scenario practice. Read the full study method.