Researchers find machine learning models still struggle to detect hate speech | Machine learning - VentureBeat

Kyle Wiggers, writes about artificial intelligence for VentureBeat notes, Detecting hate speech is a task even state-of-the-art machine learning models struggle with.

Photo: Ian Williams via Flickr

That’s because harmful speech comes in many different forms, and models must learn to differentiate each one from innocuous turns of phrase. Historically, hate speech detection models have been tested by measuring their performance on data using metrics like accuracy. But this makes it tough to identify a model’s weak points and risks overestimating a model’s quality, due to gaps and biases in hate speech datasets.

In search of a better solution, researchers at the University of Oxford, the Alan Turing Institute, Utrecht University, and the University of Sheffield developed HateCheck, an English-language benchmark for hate speech detection models created by reviewing previous research and conducting interviews with 16 British, German, and American nongovernmental organizations (NGOs) whose work relates to online hate. Testing HateCheck on near-state-of-the-art detection models — as well as Jigsaw’s Perspective tool — revealed “critical weaknesses” in these models, according to the team, illustrating the benchmark’s utility.

HateCheck’s tests canvass 29 modes that are designed to be difficult for models relying on simplistic rules, including derogatory hate speech, threatening language, and hate expressed using profanity...

The researchers suggest targeted data augmentation, or training models on additional datasets containing examples of hate speech they didn’t detect, as one accuracy-improving technique. But examples like Facebook’s uneven campaign against hate speech show significant technological challenges. Facebook claims to have invested substantially in AI content-filtering technologies, proactively detecting as much as 94.7% of the hate speech it ultimately removes. But the company still fails to stem the spread of problematic posts, and a recent NBC investigation revealed that on Instagram in the U.S. last year, Black users were about 50% more likely to have their accounts disabled by automated moderation systems than those whose activity indicated they were white.

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Source: VentureBeat