The linked post is itself linked to the API in question: Perspective [0].
The site has a demo where you can slide the "toxicity" from left to right and view the resulting bucket of comments sourced from various topics of discussion (Brexit, Climate Change, US Elections). You can also provide your own snippet and get a rating.
Obviously this kind of tool will sustain a lot of discussion about what "toxicity" is and how a ML system could infringe on speech by applying bias, either from the engineers building and evaluating the system, or from the inherent bias in data that Google themselves have already identified as a major problem. [1] For what it's worth, Perspective's site offers a definition of "toxic": "a rude, disrespectful, or unreasonable comment that is likely to make you leave a discussion." The training dataset was ostensibly built by having humans rate comments on a toxicity scale based on that definition.
It seems as though the idea is to penalize poorly thought-out or inflammatory comments while encouraging in-depth or at least more measured responses. I think we can all agree that that is an attractive system to build: the main question will be if it can be done in a fair way and what latent problems might exist in such a system.
[0] https://www.perspectiveapi.com/
[1] https://developers.googleblog.com/2018/04/text-embedding-mod...
The site has a demo where you can slide the "toxicity" from left to right and view the resulting bucket of comments sourced from various topics of discussion (Brexit, Climate Change, US Elections). You can also provide your own snippet and get a rating.
Obviously this kind of tool will sustain a lot of discussion about what "toxicity" is and how a ML system could infringe on speech by applying bias, either from the engineers building and evaluating the system, or from the inherent bias in data that Google themselves have already identified as a major problem. [1] For what it's worth, Perspective's site offers a definition of "toxic": "a rude, disrespectful, or unreasonable comment that is likely to make you leave a discussion." The training dataset was ostensibly built by having humans rate comments on a toxicity scale based on that definition.
It seems as though the idea is to penalize poorly thought-out or inflammatory comments while encouraging in-depth or at least more measured responses. I think we can all agree that that is an attractive system to build: the main question will be if it can be done in a fair way and what latent problems might exist in such a system.
[0] https://www.perspectiveapi.com/
[1] https://developers.googleblog.com/2018/04/text-embedding-mod...