Is bias in AI self-reinforcing? Decision-making systems that impact criminal justice, financial institutions, human resources, and many other areas often have bias. This is especially true of algorithmic systems that learn from historical data, which tends to reflect existing societal biases. In many high-stakes applications, like hiring and lending, these decision-making systems may even reshape the underlying populations. When the system is retrained on future data, it may become not less but more detrimental to historically disadvantaged groups.

Common fallacies about markets claim markets are unpredictable. However, chaos theory together with powerful algorithms proves such statements are wrong. Markets are chaotic systems with complex dynamics, yet to a certain extent we can make valid stock market forecasts. Using these forecasts generated by cutting-edge predictive algorithms together with a careful risk management strategy may give a trader a significant competitive advantage. Looking at the common fallacies about stock markets, we can see two major groups.

Earlier this month, when we set about to demystify some of the worst business jargon at the World Economic Forum in Davos, we could not have imagined it would hit so many of our readers' raw nerves. Hundreds felt compelled to get in touch with their own submissions, some unprintable, but the best of which we have "outlined" below. There was, of course, plenty of criticism of our selections, with many objecting to the singling out of "benchmarking" - a term that has been in use in many disciplines for several decades - and a passionate debate about the precise meaning of "negative feedback loops", more of which later. But perhaps the wittiest critique came from Charles Crowe, who maintains that "all these explanations lack granularity and do not contain metrics sufficient to let us know if we need a new paradigm". We have taken that on board, Charles.

I've had this book on pre-order since spring and it finally arrived on Friday. I subsequently devoured it over the weekend. The book lays out a clear and compelling case for how data-driven algorithms can become -- in contrast to their promise of amoral objectivism -- efficient means for reproducing and even exacerbating social inequalities and injustices. From predictive policing and recidivism risk models to targeted marketing for predatory loans and for-profit universities, O'Neil explains how to recognize WMDs by 3 distinct features: The taxonomy provides a simple framework for identifying WMDs in the wild. However, importantly for data scientists and other data practitioners, it forms a checklist (or rather an anti-checklist) to keep in mind when developing models that will be deployed into the real world.