The Future of Ethics might be hanging on that #AI training dataset

With algorithms playing an increasingly more important role in business transactions, from online retail to innovative brick-and-mortar; from structuring dispersed - and often not standardized - electronic health records, to diagnosing patients and connecting them with the right specialist; from autonomous vehicles deciding between saving the life of a passenger on-board or a pedestrian on a road side, many are warming up to the idea of an AI regulatory framework, which will never happen soon enough. But as the framework is far from being ready, companies should embrace an AI based not only on possibilities - what we can do - but also on ethical implication - what we should do not pursue. The importance is underscored by two examples, that made it to mainstream media: Amazon scrapping its HR-related AI project because it showed recruiting bias, and Equivant / Northpointe which had to kill their machine-learning for parole recommendation, because of wrong - biased - recommendations on prisoners. The risks should not underestimated. In an article on the MIT Sloan Review of August 2018, Davenport and Foutty identify seven attributes of AI-driven Leaders, or as I prefer to call them, of leaders in the era of AI.