A Day in the Life of an AI project (privacy design and AI phases)

Great presentation that breaks down what needs to be considered from a privacy point of view in the different phases of an AI project.

My hope is to turn these into a “checklist” for new AI experiments that are run on pre-assessed AI platforms. (I’m very interested in comments).

Full slides from DPC19 :


Phases of an AI project

  • Scoping
    • Problem identification
    • Impact of the AI?
    • Purpose limitation
    • Planning of solution & resources
  • Identify Data Sources
    • Getting access, data transfer
    • Compliance requirements for the data
    • Data minimization & pseudonymization
  • Data Pre-Processing
    • Exploratory Data Analysis
    • Feature selection (data minimization)
    • Feature engineering
    • Anonymization/pseudonymization
  • Modeling
    • Training, validation, testing
    • Does the model generalize well? (Test for bias/variance)
    • Support explanation
  • Deployment
    • Re-identification risk: Will the analysis or model be published?
    • Explanation to domain experts and/or data subjects
    • Incremental learning
    • Human-in-the-loop
  • Request of data subjects
    • Rights to get an explanation
    • Right to be forgotten