Artificial intelligence is already helping people do things like type faster texts and take better pictures, and it’s increasingly being used to make even bigger decisions, such as who gets a new job and who goes to jail. That’s prompting researchers across Microsoft and throughout the machine learning community to ensure that the data used to develop AI systems reflect the real world, are safeguarded against unintended bias and handled in ways that are transparent and respectful of privacy and security.
Data is the food that fuels machine learning. It’s the representation of the world that is used to train machine learning models, explained Hanna Wallach, a senior researcher in Microsoft’s New York research lab. Wallach is a program co-chair of the Annual Conference on Neural Information Processing Systems from Dec. 4 to Dec. 9 in Long Beach, California. The conference, better known as “NIPS,” is expected to draw thousands of computer scientists from industry and academia to discuss machine learning – the branch of AI that focuses on systems that learn from data.
“We often talk about datasets as if they are these well-defined things with clear boundaries, but the reality is that as machine learning becomes more prevalent in society, datasets are increasingly taken from real-world scenarios, such as social processes, that don’t have clear boundaries,” said Wallach, who together with the other program co-chairs introduced a new subject area at NIPS on fairness, accountability and transparency. “When you are constructing or choosing a dataset, you have to ask, ‘Is this dataset representative of the population that I am trying to model?’”