The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. AI has the power to transform countless industries -- including the healthcare, banking, insurance, and public service sectors, to name just a few -- by introducing new efficiencies and revealing new opportunities for companies to solve problems. Any organisation that is considering adopting AI must first be willing to trust in AI technology. Organisations must feel confident that human error did not inadvertently contribute to AI bias that resulted in inaccurate or misleading findings. This new DataRobot whitepaper, "Data Science Fails: Building AI You Can Trust", outlines eight important lessons that organisations must understand to follow best data science practices and ensure that AI is being implemented successfully.

There is a saying, "Be careful what you wish for; you might just receive it." One of the first-ever stories to exemplify this saying comes from Greek mythology. The story goes that Midas earned the favor of Silenus, a satyr. Silenus offered Midas a wish, and Midas wished that everything he touched would turn to gold. Immediately, Midas put the power to the test, touching a rose and turning it into solid gold.

When I was young, I took a packed lunch to school every day, and since I grew up in Australia, my packed lunch would include a couple of Vegemite sandwiches. Unless you grew up in Australia, you've probably never tasted it. And judging by this American's first taste reaction of "Oh, that's bad!", you probably wouldn't like the taste if you tried it out. But I loved my Vegemite sandwiches, and they were my one-and-only lunchtime choice, no matter what the circumstances. While this blog isn't about Vegemite, it is related to lunch, specifically the no free lunch theorem.

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. AI has the power to transform countless industries -- including the healthcare, banking, insurance, and public service sectors, to name just a few -- by introducing new efficiencies and revealing new opportunities for companies to solve problems. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology. Organizations must feel confident that human error did not inadvertently contribute to AI bias that resulted in inaccurate or misleading findings. The new DataRobot whitepaper, Data Science Fails: Building AI You Can Trust, outlines six common important lessons that organizations must understand to follow best data science practices and ensure that AI is being implemented successfully.

Similarly, in the paper "Towards Debiasing Fact Verification Models," a related group of researchers identified a problem in the FEVER database that caused the unexpected result that claim-only classifiers performed competitively against evidence-aware classifiers. The authors identify strong cues for predicting labels solely based on the claims made. The FEVER dataset was constructed using crowdsourcing, allowing human bias to introduce human artifacts into the data. For example, the presence of negatively phrased statements was highly predictive of text flagged as fake news, yet this was not representative of fake news in the real world. Modern machine learning algorithms quickly identified these artifacts of human bias.