Module 1 - Lesson 8: Bootstrapping and the risks of algorithmic decision-making

[turukawa]

ETHICS

Differentiate as to when algorithmic decision-making has the potential to cause harm.

Automation lowers costs but also leads to “machine says no” situations. What can be done to “soften” this?
Example: automated diagnostics, insurance claims, filtered views (e.g. Facebook/ Twitter)

CURATION

Determine appropriate methods to classify and process data to aid discovery and analysis.

“Aboutness” of data is critical to discovery, but data creators don’t always classify appropriately.
Discussion of methods to classify aboutness, both manual and automatic, and recognising that automation may misclassify (since can’t understand context).
Eg. automated trading platforms response to news “events”.

ANALYSIS

Simulate resampling by generating new random samples using bootstrapping.

Bootstrap method, and using confidence intervals.

PRESENTATION

Present replicated samples as a stacked interval chart, displaying median and interval.

Chart is a variation of that used in meta-analysis for comparison.

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CASE STUDY

Demonstrate how samples may show a tremendous bias, even inadvertently (hence why resampling important) and ties in above. E.g. patient waiting times.