Gavin Chait
Gavin Chait
## ETHICS *Consider the strengths and limitations of machine intelligence in the Chinese Room Experiment.* John Searle’s Chinese Room Experiment; strong vs weak AI. Intentionality and consciousness. Example: Facebook Go;...
## 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....
## ETHICS *Judge ethical responses – and states of reflective equilibrium – when justifying causing harm based on analysis.* John Rawls’ “Reflective equilibrium” and the trolley problems of “tractor” and...
## ETHICS *Evaluate methods for balancing right and wrong in trolley problems.* Introduction to trolley problems and the conflicting ethical considerations they raise. Foundation for the module. Example: Would you...
## ETHICS *Appraise the challenges inherent in evaluating and communicating analysis and results.* Analytical models are based on complex data. If any of these are opaque or inaccessible, then the...
Module 1 - Lesson 9: Sample robustness, central limit theory, and the ethics and abuses of p-hacking
## ETHICS *Appraise the risk of bias in p-hacking, and the risk to scientific self-correction from stigmatising researchers.* P-hacking happens unintentionally; review of mechanisms by which they occur and how...
## 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...
## ETHICS *Assess the risks of population exclusion and hazardous externalities on data quality.* Statistics are not collected in a vacuum. They can inform and lead to policy implementation. Consider...
## ETHICS *Determine the impact of marginalised populations in data sampling, and risk of spurious correlations.* Sampling methods and impacts on likely results, danger of spurious correlations. Examples: how to...
## ETHICS *Recognise issues in analysing and exploring data for analysis.* Importance of prepublication on bias; will find correlation give law of large numbers. Example: Abortion and crime. ## CURATION...