Media Coverage
Rewire’s work to create a safer internet is informed by our innovative research. Our team regularly contributes to academic and policy discussions about AI and online safety, and our work often makes its way onto leading news and media platforms. If you would like to feature us or our work, drop us a line at info@rewire.online.
AUGUST 2022
DCMS Spotlights Rewire in ‘UK Safety Tech Sector: 2022 Analysis’ Report
Rewire is spotlighted as 1 of 5 firms in the fast-growing online safety industry. Digital Minister Damian Collins says: “Making the online world safer is not only the right thing to do, it’s good for business.”
FEBRUARY 2022
UNESCO International Research Center Spotlights In 2022 Global Top 10 Outstanding AI Solutions
Rewire features as one of the Top 10 ‘Outstanding’ AI solutions to solving Sustainable Development Goals, as chosen by the UNESCO International Research Centre for Artificial Intelligence.
JULY 2021
Why Was My Tweet About Football Labelled Abusive?
How do social media platforms like Twitter decide that a violation has occurred? And who or what is behind these decisions? We spoke to BBC News about our research to help them answer these questions.
JULY 2021
Social Media Fails to Curb Racist Emojis Aimed at Soccer Stars
In light of the hate directed at the England football team online, we spoke to Bloomberg News about how speech detection models, including Google’s own, Jigsaw, respond to offensive emojis.
JUNE 2021
AI Still Sucks At Moderating Hate Speech
In this feature for the MIT Technology Review, we discussed the limitations of language models, the trade-offs between under- and over-identifying hate speech, and how we might overcome them.
JUNE 2021
AI Algorithms Get Tripped Up in Fighting Hate Speech
We spoke to the Wall Street Journal about the effectiveness of online hate-speech filters, and why they don’t always catch hateful speech.
JANUARY 2021
Researchers Find Machine Learning Models Still Struggle To Detect Hate Speech
We spoke to VentureBeat about our research, the limitations of current hate speech detection models, and how they might be developed in the future.
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