Perspective API, the tool developed by Google, is an open source project aiming to improve participation and the quality of online discussion at scale. It uses a convolutional neural network (CNN) on both the word and sentence level to determine how close a sentence is to the toxic comments in the training set, and to calculate a score in relation to its toxicity. Currently it has three usages: moderation helps editors process comments faster by quickly identifying the toxic elements; authorship helps writers understand the impact of what they write; and readership helps readers understand the comments.
At the end of the talk, CJ elaborated on how Perspective API measures and mitigates bias. Quite often, certain identity terms are heavily represented in the toxic language text. There are not enough examples of the identity terms in positive, non-toxic phrases, and too many examples of the identity terms in toxic phrases. This causes bias in the model, resulting in phrases containing these identity terms being mis-classified (in some instances) as “toxic.” To reduce this kind of bias, it is important to rebalance the existing data with extra training data. For this purpose, Google created Project Respect for researchers to submit training samples.
Back in April, our NYC-based engineering team started hosting a series of technical meetup events to engage local tech experts in sharing their knowledge, best practices, and culture. Thanks to the strong support from the community, the two previous events were well received and brought together hundreds of passionate engineers from across the NYC area.
For this latest event, big thanks to the hosts, Siva Visakan Sooriyan, Xiaoqiang Luo, and Anita Desai, for organizing the meetup. And many thanks to the speakers, Xiaoqiang Luo, Yiye Zhang, and CJ Adams, the volunteers from LinkedIn, and all the attendees for making the meetup a success!
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