Explainable AI and the Future of Machine Learning

As the'AI era' of increasingly complex, smart, autonomous, big-data-based tech comes upon us, the algorithms that fuel it are getting under more and more scrutiny. Whether you're a data scientist or not, it becomes obvious that the inner workings of machine learning, deep learning, and black-box neural networks are not exactly transparent. In the wake of high-profile news reports concerning user data breaches, leaks, violations, and biased algorithms, that is rapidly becoming one of the biggest -- if not the biggest -- sources of problems on the way to mass AI integration in both the public and private sectors. Here's where the push for better AI interpretability and explainability takes root. Already a focal point of machine learning consulting and a notable topic in the 2019 AI discussions, it's only likely to accelerate and become one of the central conversations of 2020 regarding the questions of both security and ethics of artificial intelligence. The days of the ideas like'machines will become too smart and independent and will rise against humanity' are long behind us, with the sentiment firmly relegated to the realm of science fiction and entertainment.