How should government and society come together to address the challenge of regulating artificial intelligence? What approaches and tools will promote innovation, protect society from harm and build public trust in AI? Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution. Algorithms are already being applied to improve predictions, optimize systems and drive productivity in many other sectors. However, early experience shows that AI can create serious challenges. Without proper oversight, AI may replicate or even exacerbate human bias and discrimination, cause potential job displacement, and lead to other unintended and harmful consequences.

If there was a data science hall of fame, it would have a section dedicated to labeling. The labelers' monument could be Atlas holding that large rock symbolizing their arduous, detail-laden responsibilities. ImageNet -- an image database -- would deserve its own stele. Just thinking about it makes you tired. While labeling is not launching a rocket into space, it's still seriously business. Labeling is an indispensable stage of data preprocessing in supervised learning. Historical data with predefined target attributes (values) is used for this model training style. An algorithm can only find target attributes if a human mapped them. Labelers must be extremely attentive because each mistake or inaccuracy negatively affects a dataset's quality and the overall performance of a predictive model. How to get a high-quality labeled dataset without getting grey hair?

If there was a data science hall of fame, it would have a section dedicated to labeling. The labelers' monument could be Atlas holding that large rock symbolizing their arduous, detail-laden responsibilities. ImageNet -- an image database -- would deserve its own stele. Just thinking about it makes you tired. While labeling is not launching a rocket into space, it's still seriously business.