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Content moderation comes to every content platform eventually, and platforms are starting to realize this faster than ever.

Nothing symbolizes this shift as neatly as Facebook’s decision in October (and Twitter’s shortly after) to start banning Holocaust denial. Almost exactly a year earlier, Zuckerberg had proudly tied himself to the First Amendment in a widely publicized “stand for free expression” at Georgetown University.

The evolution continues. Facebook announced earlier this month that it will join platforms such as YouTube and TikTok in removing, not merely labeling or down-ranking, false claims about COVID-19 vaccines.

the pandemic also showed that complete neutrality is impossible. Even though it’s not clear that removing content outright is the best way to correct misperceptions, Facebook and other platforms plainly want to signal that, at least in the current crisis, they don’t want to be seen as feeding people information that might kill them.

When internet platforms announce new policies, assessing whether they can and will enforce them consistently has always been difficult. In essence, the companies are grading their own work. But too often what can be gleaned from the outside suggests that they’re failing.

It tweaked its algorithm to boost authoritative sources in the news feed and turned off recommendations to join groups based around political or social issues. Facebook is reversing some of these steps now, but it cannot make people forget this toolbox exists in the future

As platforms grow more comfortable with their power, they are recognizing that they have options beyond taking posts down or leaving them up. In addition to warning labels, Facebook implemented other “break glass” measures to stem misinformation as the election approached.

Platforms don’t deserve praise for belatedly noticing dumpster fires that they helped create and affixing unobtrusive labels to them

Warning labels for misinformation might make some commentators feel a little better, but whether labels actually do much to contain the spread of false information is still unknown.

News reporting suggests that insiders at Facebook knew they could and should do more about misinformation, but higher-ups vetoed their ideas. YouTube barely acted to stem the flood of misinformation about election results on its platform.

Even before the pandemic, YouTube had begun adjusting its recommendation algorithm to reduce the spread of borderline and harmful content, and is introducing pop-up nudges to encourage user

And if 2020 finally made clear to platforms the need for greater content moderation, it also exposed the inevitable limits of content moderation.

Down-ranking, labeling, or deleting content on an internet platform does not address the social or political circumstances that caused it to be posted in the first place

even the most powerful platform will never be able to fully compensate for the failures of other governing institutions or be able to stop the leader of the free world from constructing an alternative reality when a whole media ecosystem is ready and willing to enable him. As Renée DiResta wrote in The Atlantic last month, “reducing the supply of misinformation doesn’t eliminate the demand.”

Even so, this year’s events showed that nothing is innate, inevitable, or immutable about platforms as they currently exist. The possibilities for what they might become—and what role they will play in society—are limited more by imagination than any fixed technological constraint, and the companies appear more willing to experiment than ever.

“Very long chat sessions can confuse the underlying chat model in the new Bing,” Microsoft said in a blog on Friday. “To address these issues, we have implemented some changes to help focus the chat sessions.”

Microsoft said in the Wednesday blog that Bing seems to start coming up with strange answers following chat sessions of 15 or more questions after which it can become repetitive or respond in ways that don’t align with its designed tone.

The company said it was trying to train the technology to be more reliable. It is also considering adding a toggle switch, which would allow users to decide whether they want Bing to be more or less creative with its responses.

Microsoft leaders felt “enormous urgency” for it to be the company to bring this technology to market, because others around the world are working on similar tech but might not have the resources or inclination to build it as responsibly, says Sarah Bird, a leader on Microsoft’s responsible AI team.

One common starting point for such models is what is essentially a download or “scrape” of most of the internet. In the past, these language models were used to try to understand text, but the new generation of them, part of the revolution in “generative” AI, uses those same models to create texts by trying to guess, one word at a time, the most likely word to come next in any given sequence.

Wide-scale testing gives Microsoft and OpenAI a big competitive edge by enabling them to gather huge amounts of data about how people actually use such chatbots. Both the prompts users input into their systems, and the results their AIs spit out, can then be fed back into a complicated system—which includes human content moderators paid by the companies—to improve it.

, being first to market with a chat-based AI gives these companies a huge initial lead over companies that have been slower to release their own chat-based AIs, such as Google.

rarely has an experiment like Microsoft and OpenAI’s been rolled out so quickly, and at such a broad scale.

Among those who build and study these kinds of AIs, Mr. Altman’s case for experimenting on the global public has inspired responses ranging from raised eyebrows to condemnation.

The fact that we’re all guinea pigs in this experiment doesn’t mean it shouldn’t be conducted, says Nathan Lambert, a research scientist at the AI startup Huggingface.

“I would kind of be happier with Microsoft doing this experiment than a startup, because Microsoft will at least address these issues when the press cycle gets really bad,” says Dr. Lambert. “I think there are going to be a lot of harms from this kind of AI, and it’s better people know they are coming,” he adds.

Others, particularly those who study and advocate for the concept of “ethical AI” or “responsible AI,” argue that the global experiment Microsoft and OpenAI are conducting is downright dangerous

Celeste Kidd, a professor of psychology at University of California, Berkeley, studies how people acquire knowledge

Her research has shown that people learning about new things have a narrow window in which they form a lasting opinion. Seeing misinformation during this critical initial period of exposure to a new concept—such as the kind of misinformation that chat-based AIs can confidently dispense—can do lasting harm, she says.

Dr. Kidd likens OpenAI’s experimentation with AI to exposing the public to possibly dangerous chemicals. “Imagine you put something carcinogenic in the drinking water and you were like, ‘We’ll see if it’s carcinogenic.’ After, you can’t take it back—people have cancer now,”

Part of the challenge with AI chatbots is that they can sometimes simply make things up. Numerous examples of this tendency have been documented by users of both ChatGPT and OpenA

These models also tend to be riddled with biases that may not be immediately apparent to users. For example, they can express opinions gleaned from the internet as if they were verified facts

When millions are exposed to these biases across billions of interactions, this AI has the potential to refashion humanity’s views, at a global scale, says Dr. Kidd.

OpenAI has talked publicly about the problems with these systems, and how it is trying to address them. In a recent blog post, the company said that in the future, users might be able to select AIs whose “values” align with their own.

“We believe that AI should be a useful tool for individual people, and thus customizable by each user up to limits defined by society,” the post said.

Eliminating made-up information and bias from chat-based search engines is impossible given the current state of the technology, says Mark Riedl, a professor at Georgia Institute of Technology who studies artificial intelligence

He believes the release of these technologies to the public by Microsoft and OpenAI is premature. “We are putting out products that are still being actively researched at this moment,” he adds. 

in other areas of human endeavor—from new drugs and new modes of transportation to advertising and broadcast media—we have standards for what can and cannot be unleashed on the public. No such standards exist for AI, says Dr. Riedl.

To modify these AIs so that they produce outputs that humans find both useful and not-offensive, engineers often use a process called “reinforcement learning through human feedback.

that’s a fancy way of saying that humans provide input to the raw AI algorithm, often by simply saying which of its potential responses to a query are better—and also which are not acceptable at all.

Microsoft’s and OpenAI’s globe-spanning experiments on millions of people are yielding a fire hose of data for both companies. User-entered prompts and the AI-generated results are fed back through a network of paid human AI trainers to further fine-tune the models,

Huggingface’s Dr. Lambert says that any company, including his own, that doesn’t have this river of real-world usage data helping it improve its AI is at a huge disadvantage

In chatbots, in some autonomous-driving systems, in the unaccountable AIs that decide what we see on social media, and now, in the latest applications of AI, again and again we are the guinea pigs on which tech companies are testing new technology.

It may be the case that there is no other way to roll out this latest iteration of AI—which is already showing promise in some areas—at scale. But we should always be asking, at times like these: At what price?