Inclusive Data Science
A single person can be the source of billions of data points, whether these are generated from everyday internet use, healthcare records, wearable sensors or participation in experimental research. This vast amount of data can be used to make predictions about people and systems: what is the probability this person will develop diabetes in the next year? Will commit a crime? Will be a good employee? Is of a particular ethnicity? Predictions are simply represented by a number, produced by an algorithm. A single number in itself is not biased. How that number was generated, interpreted and subsequently used are all processes deeply susceptible to human bias and prejudices. This session will explore a philosophical perspective of data ethics and discuss practical steps to reducing statistical bias. There will be opportunity in the last section of the session for attendees to discuss and troubleshoot ethical questions from their own analyses in a ‘Data Clinic’.
Neuroimaging reproducibility - pain points and roadmap for solid and reusable results
There is a growing body of evidence that reproducibility or replication is low in neuroscience and in neuroimaging in particular, but the factors affecting studies solidity are still generally poorly understood, and the solutions are not clearly exposed to the neuroimaging scientific community. In this talk, I will review the key factors contributing to irreproducible results in neuroimaging specifically in the context of explanatory or prediction studies and propose a series of practical steps to improve the neuroimaging (and neuroscience) results robustness and re-usability.