Neural networks were invented in the '60s, but recent boosts in big data and computational power made them actually useful. A new discipline called "deep learning" has arisen that can apply complex neural network architectures to model patterns in data more accurately than ever before.
Your results are only as good as your data. Neural networks fed inaccurate or incomplete data will simply produce the wrong results. The outcomes can be both embarrassing and damaging. In two major PR debacles, Google Images incorrectly classified African Americans as gorillas, while Microsoft's Tay learned to spew racist, misogynistic hate speech after only hours training on Twitter.
Undesirable biases are implicit in our input data. Google's massive Word2Vec embeddings are built off of 3 million words from Google News. The data set makes associations such as "father is to doctor as mother is to nurse," which reflect gender bias in our language. Researchers such as Tolga Bolukbasi of Boston University have taken to human ratings on Mechanical Turk to perform "hard de-biasing" to undo such associations.
Such tactics are essential since, according to Bolukbasi, "word embeddings not only reflect stereotypes but can also amplify them." If the term "doctor" is more associated with men than women, then an algorithm might prioritize male job applicants over female job applicants for open physician positions.
Debiasing is itself a trend related to improving the quality of all data.
