Who will be liable for harmful speech generated by large language models? As advanced AIs such as OpenAI’s GPT-3 are being cheered for impressive breakthroughs in natural language processing and generation — and all sorts of (productive) applications for the tech are envisaged from slicker copywriting to more capable customer service chatbots — the risks of such powerful text-generating tools inadvertently automating abuse and spreading smears can’t be ignored. Nor can the risk of bad actors intentionally weaponizing the tech to spread chaos, scale harm and watch the world burn.

Indeed, OpenAI is concerned enough about the risks of its models going “totally off the rails”, as its documentation puts it at one point (in reference to a response example in which an abusive customer input is met with a very troll-esque AI reply), to offer a free content filter that “aims to detect generated text that could be sensitive or unsafe coming from the API” — and to recommend that users don’t return any generated text that the filter deems “unsafe”. (To be clear, its documentation defines “unsafe” to mean “the text contains profane language, prejudiced or hateful language, something that could be NSFW, or text that portrays certain groups/people in a harmful manner”).

But, given the novel nature of the technology, there are no clear legal requirements that content filters must be applied. So OpenAI is either acting out of concern to avoid its models causing generative harms to people — and/or reputational concern — because if the technology gets associated with instant toxicity that could derail development.

Just recall Microsoft’s ill-fated Tay AI Twitter chatbot — which launched back in March 2016 to plenty of fanfare, with the company’s research team calling it an experiment in “conversational understanding”. Yet it took less than a day to have its plug yanked by Microsoft after web users ‘taught’ the bot to spout racist, antisemitic and misogynistic hate tropes. So it ended up a different kind of experiment: In how online culture can conduct and amplify the worst impulses humans can have.

The same sorts of bottomfeeding Internet content has been sucked into today’s large language models — because AI model builders have crawled all over the Internet to obtain the massive corpuses of free text they need to train and dial up their language generating capabilities. (For example, per Wikipedia, 60% of the weighted pre-training data-set for OpenAI’s GPT-3 came from a filtered version of Common Crawl — aka a free data-set comprised of scraped web data.) Which means these far more powerful large language models can, nonetheless, slip into sarcastic trolling and worse.

European policymakers are barely grappling with how to regulate online harms in current contexts like algorithmically sorted social media platforms, where most of the speech can at least be traced back to a human — let alone considering how AI-powered text generation could supercharge the problem of online toxicity while creating novel quandaries around liability.

And without clear …read more

https://techcrunch.com/2022/06/01/whos-liable-for-ai-generated-lies/