Description
ETHICS
Examine notions of fairness between competing interest groups and amongst inexpert stakeholders.
Ultimate games, and notions of fairness. What happens when data and analysis contradict notions of fairness. Which “fairness” for whom, and why?
Examples: carbon emissions and accountability; GM foods and ownership; medicines and CRISPR; i.e. what is “fair” when most people cannot understand the data or methodology?
CURATION
Consider the risks and requirements for data disclosure when legal notions of privacy change.
Fairness in data curation; non-competing disclosure when some authorities / data owners refuse to publish; Freedom of Information and Tony Blair’s regret.
Could it appear on the front page of the New York Times … would you be embarrassed?
ANALYSIS
Identify appropriate variables for inclusion in a model and consider the variability of predictions.
Prediction intervals, and variability of prediction; identifying variables for exclusion, and two model selection. P-value approach to adjust R2, and confidence intervals …
“All models are wrong, but some are useful.”
PRESENTATION
Present variability of predictions to reflect confidence and uncertainty in the underlying data and methods.
Continuing from #12.
CASE STUDY
Continuing previous leukaemia case study; raise considerations about notifiability (HIV, Ebola … Trump’s response to banning re-entry of volunteer nurses).