Module 2 - Lesson 7: Counterfactual consequences, and implementing, testing and optimising classifiers

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ETHICS

Develop mechanisms to explore the risks of counterfactual and unknowable consequences.

Counterfactuals (pp 58 in Ethics), and actual vs expected consequences; immediate vs distant; Example: Google AI in China potentially used to support police state.

CURATION

Determine methods to permit data sources who are private individuals to access, modify or remove their personal records.

Individual access for inspecting / correction of data; Know Your Customer, and methods for secure identification of “owners” / sources of data; Example: credit reports on individuals, some countries require users to have access. What about patient records?

ANALYSIS

Implement, test and optimise classifiers using a variety of methods.

Multiple attributes to k-nearest, and a return to linear regression; plus, maybe, stochastic optimisation (from “Collective Intelligence”)?

PRESENTATION

Reveal classification clustering in multiple dimensions on 2D plots using random “jittering”.

Adding a very small random “jitter” to scatter plots to ensure that overlaps can be seen.

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CASE STUDY

Continue with cancer data, or a new study from Dataverse.