2019 Shortlist for the Royal Society Prize for Science Books

Since 1988, the Royal Society has celebrated outstanding popular science writing and authors. Each year, a panel of expert judges choose the book that they believe makes popular science writing compelling and accessible to the public. Over the decades, the Prize has celebrated some notable winners including Bill Bryson and Stephen Hawking. The author of the winning…

Artificial Stupidity – by Vincent Warmerdam @PyData 2019 London

PyData is famous for it’s great talks on machine learning topics. This 2019 London edition, Vincent Warmerdam again managed to give a super inspiring presentation. This year he covers what he dubs Artificial Stupidity™. You should definitely watch the talk, which includes some great visual aids, but here are my main takeaways: Vincent speaks of…

Survival of the Best Fit: A webgame on AI in recruitment

Survival of the Best Fit is a webgame that simulates what happens when companies automate their recruitment and selection processes. You – playing as the CEO of a starting tech company – are asked to select your favorite candidates from a line-up, based on their resumés. As your simulated company grows, the time pressure increases,…

Propensity Score Matching Explained Visually

Propensity score matching (wiki) is a statistical matching technique that attempts to estimate the effect of a treatment (e.g., intervention) by accounting for the factors that predict whether an individual would be eligble for receiving the treatment. The wikipedia page provides a good example setting: Say we are interested in the effects of smoking on…

Privacy, Compliance, and Ethical Issues with Predictive People Analytics

November 9th 2018, I defended my dissertation on data-driven human resource management, which you can read and download via this link. On page 149, I discuss several of the issues we face when implementing machine learning and analytics within an HRM context. For the references and more detailed background information, please consult the full dissertation. More interesting reads on ethics in machine learning can be found here….

Simpson’s Paradox: Two HR examples with R code.

Simpson (1951) demonstrated that a statistical relationship observed within a population—i.e., a group of individuals—could be reversed within all subgroups that make up that population. This phenomenon, where X seems to relate to Y in a certain way, but flips direction when the population is split for W, has since been referred to as Simpson’s…

IBM’s Watson for Oncology: A Biased and Unproven Recommendation System in Cancer Treatment?

The below reiterates and summarizes this Stat article. Recently, I addressed how bias may slip into Machine Learning applications and this weekend I came across another real-life example: IBM’s Watson, specifically Watson for Oncology. With a single machine, IBM intended to tackle humanity’s most vexing diseases and revolutionize medicine and they quickly zeroed in on a high-profile target:…

Video: Bias in Machine Learning

Mainstream media have caught onto the difficulties of machine learning. Most saliently, they just love to report how AI and bots can be as racist, discriminatory, or biased as humans. Some examples: Microsoft’s racist Twitter bot (Verge, 2016) Gender-biased text mining AI (Guardian, 2017) Racist criminal profiling bot (ProPublica, 2016) Google’s Sentiment Analyzer Thinks Being…