On the 17 of July 2020, the High-Level Expert Group on Artificial Intelligence (AI HLEG) presented their final Assessment List for Trustworthy Artificial Intelligence.
“This study addresses the relation between the EU General Data Protection Regulation (GDPR) and artificial intelligence (AI). ”
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).
Phases of an AI project
- 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
- Training, validation, testing
- Does the model generalize well? (Test for bias/variance)
- Support explanation
- Re-identification risk: Will the analysis or model be published?
- Explanation to domain experts and/or data subjects
- Incremental learning
- Request of data subjects
- Rights to get an explanation
- Right to be forgotten
IAPP conference presentation that breaks down what needs to be considered from a privacy point of view in the different phases of an AI project.
This could be the basis for a “checklist” for new AI experiments that are run on pre-assessed AI platforms.
Very nice summary paper on AI and AI bias, which covers many examples, including the Amazon AI recruitment tool bias case.
Includes a very nice summary of GDPR expectations on AI
Light, high-level presentation at a FDA event in 2017 (?), with some *easy* examples of *bias* and *potential errors/issues*.. (also some pointer to GDPR discussion)