Should America do as China does when it comes to pre-testing generative AI before allowing public … [+] release, or is that a sour idea?
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In today’s column, I aim to closely examine a rather thought-provoking question about what might happen if the United States decided to require a pre-test or prior validation of generative AI apps before they were permitted to be publicly released, including doing so for well-known and wildly popular favorites such as ChatGPT, GPT-4, Gemini, Bard, Claude, and others.
The basis or impetus to consider this intriguing notion is due to a recent news report that China is doing just that already, stipulating that generative AI or large language models must meet certain governmental provisions and prescribed tests before legally hitting the streets. Is China doing the right thing? Should America do the same? Or is China doing something that seemingly befits China, but perhaps is akin to a square peg trying to fit in a round hole regarding a similar approach for the US?
Let’s talk about it.
Before we leap into the details, allow me to go into my customary opening remarks.
For my ongoing readers, in today’s column, I am continuing my in-depth series about the international and global perspectives underpinning advances and uses of generative AI.
I’ve previously covered for example the sobering matter of humanity-saving efforts intended to establish global multilateral unity on the allowed uses and safety-promoting constraints of AI, see the link here, along with ways that nations are seeking to be worldwide powerhouses by vastly adopting the latest in AI, see the link here. Please be aware that countries big and small are desirous of using their potential prowess and progress in AI as watch-out geopolitical bargaining chips, see the link here. You might also find of notable interest a CBS 60 Minutes episode that recently examined crucial facets of AI, see the link here (I am honored and pleased to indicate that I was featured in the episode, see the link here).
Generative AI Is Here And Staying Here
It seems that nearly everyone has heard about generative AI, and many have used a generative AI app at one time or another. OpenAI, the maker of ChatGPT, boasts that they have over one hundred million weekly active users of their wares. If we were to add up counts of users across all the major generative AI apps, the total tally is undoubtedly astounding and would vividly highlight how pervasive generative AI has become.
Before I jump into the mind-bending question spurring today’s discussion, I’d like to first make sure we are up-to-speed about what generative AI is. I will briefly cover in general the ins and outs of generative AI and large language models (LLMs), doing so to make sure we are on the same page when it comes to discussing the matter at hand.
The crux of generative AI is that your input of text-entered prompts is used to produce or generate a flowing response that seems quite fluent. This is a remarkable overturning of the old-time natural language processing (NLP) that used to be stilted and awkward to use (think of conventional Siri and Alexa), which has been shifted into a new version of NLP fluency of an at times startling or amazing caliber.
The customary means of achieving modern generative AI involves using a large language model (LLM) as the key underpinning.
In brief, a computer-based model of human language is established that in the large has a large-scale data structure and does massive-scale pattern-matching via a large volume of data used for initial data training. The data is typically found by extensively scanning the Internet for lots and lots of essays, blogs, poems, narratives, and the like. The mathematical and computational pattern-matching homes in on how humans write, and then henceforth generates responses to posed questions by leveraging those identified patterns.
It is said to be computationally mimicking the writing of humans.
I think that is sufficient for the moment as a quickie backgrounder. Take a look at my extensive coverage of the technical underpinnings of generative AI and LLMs at the link here and the link here, just to name a few.
Making Generative AI Palatable For Public Use
You might not realize that before the release of ChatGPT in November 2022, there had been quite a number of generative AI and LLM releases that were placed into the marketplace. Does that surprise you? It might.
I doubt you would know about those instances. The reason why is that they were immediately pounced on by avid first-out-the-gate users and AI insiders who were able to readily prod the AI into spewing hate speech and other toxic sayings, see my coverage at the link here. By and large, the AI makers had to quickly pull their AI wares out of the public eye and took a beating for having seemingly jumped the gun on being suitably ready for release.
Why did generative AI in the early days flagrantly produce foul outputs?
Easy-peasy answer.
Consider that generative AI is principally based on content found on the Internet. Please give that innocuous indication a heartfelt moment of critical thought. We all know that the Internet is a vast source of incredible information that can be tremendously helpful and serve as a boost to sharing knowledge around the world. Happy face. The Internet can also contain the worst of the worst. There is misinformation. There is disinformation. There is blatantly offensive material. Yes, the Internet can be an online sewer. Sad, sad, sad face.
Here’s what OpenAI fortuitously did to try and cope with the badness of pattern-matching on the Internet at large, doing so before putting ChatGPT into the public sphere.
Numerous refinements were undertaken to the raw version of ChatGPT before they released a filtered or refined ChatGPT to the public. For example, they made use of a now popular technique known as RLHF (reinforcement learning via human feedback), see my detailed discussion at the link here. RLHF is a significant means of trying to get generative AI to avoid producing offensive essays or repeating false facts that might have been garnered during the data training stage.
It works like this.
Before public release, specially hired human reviewers are shown various generative AI outputs, and the human reviewers rate the outputs to provide feedback to the AI algorithms. The algorithms try to pattern-match what is considered acceptable by the human reviewers versus what is unacceptable. If a human reviewer for example marks that it is inappropriate to reveal how to build a Molotov cocktail, this is used to mathematically and computationally suppress the AI production of such essays or statements henceforth.
This sounds great and appears to be an ironclad way to solve the problem of reining in generative AI. Unfortunately, life and the world never seem to be that simple. Human reviewers are unlikely to catch every possible permutation and combination of the ways that generative AI will produce an adverse or unacceptable essay or response. Bad stuff will still get through.
Now then, some people are upset that the generative AI we use today is heavily filtered.
They heatedly point out that the techniques for reducing toxic outputs can either inadvertently or at times be purposely utilized for questionable means. Imagine this. Human reviewers chosen to do the RLHF perchance are of a certain kind of political bent. They mark as upbeat the outputs that match their preferred political candidates and political positions, while they mark as downgraded those of the other political side. The result will be that the generative AI is skewed in a particular political direction, see my analysis at the link here.
A vital call by those who are disturbed by how generative AI is being shaped and directed has led to a fervent desire to have AI makers make available their generative AI entirely in the raw, see my coverage at the link here. The idea is that we ought to be able to see the “before” and the “after”. Let us play with the generative AI that existed before the filtering and be able to compare this to the version that is promulgated after the filtering.
It seems doubtful that AI makers would willingly allow their earliest versions to be readily accessible.
The reason is twofold. First, they would undoubtedly experience all manner of societal and cultural attacks that the raw version likely is abrasive and foul at the get-go. That would be the first body blow landed on the AI makers. The second blow would occur once the comparison revealed how the AI maker secretly or behind-the-scenes seemed to have censored the AI. This would be a twofer that few AI makers would business-wise survive.
For now, we seem to be relatively accepting of the prevailing condition that we are using generative AI that has been heavily filtered, presumably for our safety from seeing foul and toxic outputs. The benefit is that we don’t customarily encounter ugly stuff when using generative AI in any everyday fashion. You can still try to stoke generative AI into saying toxic things, see my coverage at the link here, but most of the time you are spared that angst.
Is it worth it to us that there might also be unseen embedded biases along all manner of lines, such as by politics, demographic factors, and so on?
Such biases might not be apparent. The AI could be using those under-the-hood patterns and meanwhile suppressing saying so. I explain how this can and is happening, see the link here. For now, we generally go along with this.
Deciding If Generative AI Ready For Prime Time
I have led you step-by-step to a very important juncture.
In the United States, AI makers can pretty much release generative AI in whatever raw or refined fashion that they wish. We have seen that the public prefers generative AI that is polite and civil in language and tone. Voila, the AI makers have listened to the marketplace and honed their generative AI accordingly.
There aren’t any tailored federal laws that per se stipulate what generative AI can and cannot be allowed to emit (well, we shall see whether this holds, see my analysis at the link here and the link here). We have taken a near freedom-of-speech approach to our generative AI. Let the market decide what is acceptable. If an AI maker puts out a generative AI that is completely one-sided on the political spectrum, that’s on them to do as they wish. There might be a firestorm of complaints and calls for the AI to be taken down, but you’d have a devil of a time prevailing legally on that type of ban or exile. See my in-depth analysis at the link here about the latest in the legal twists and turns of AI.
A recent article in the Wall Street Journal (WSJ) entitled “China Puts Power Of State Behind AI – And Risks Strangling It” (Liza Lin, July 16, 2024), noted that China is taking a different tack when it comes to what is permitted via releases of generative AI (excerpts):
“Most generative AI models in China need to obtain the approval of the Cyberspace Administration of China before being released to the public.”
“The internet regulator requires companies to prepare between 20,000 and 70,000 questions designed to test whether the models produce safe answers, according to people familiar with the matter.”
“Companies must also submit a data set of 5,000 to 10,000 questions that the model will decline to answer, roughly half of which relate to political ideology and criticism of the Communist Party.”
“Generative AI operators have to halt services to users who ask improper questions three consecutive times or five times total in a single day.”
Whoa, that deserves some mindful unpacking.
Let’s do so.
First, as noted, a governmental agency in China is tasked with either approving or denying the release of generative AI models into the Chinese marketplace. Some narrow exceptions might legitimately skirt around this governance based on allowed exceptions, but these are relatively few and far between. Furthermore, any AI maker that sneaks past the governmental agency is facing quite imposing repercussions.
There is in a sense both a carrot and a stick involved. To some degree, the Chinese government is currently aiding the development of generative AI within China by providing access to hardware at low prices or sometimes free, providing data and possibly aiding in data prep, and taking other actions. That is the carrot. The stick is that if you don’t pass muster with the governmental agency, you are going to feel the determined mighty weight of the government on your back and your generative AI will never see the light of day.
How would a government agency ascertain whether a generative AI app is suitable for release?
I’m glad you asked.
In the case of China, per the above reporting, it seems that the AI maker must come up with 20,000 to 70,000 questions that when presented to their generative AI, the answers generated must be assessed as suitable to the government agency.
Ponder this, if you please.
You might on the one hand say that this is perfectly sensible. Shifting for the sake of discussion into a US context, suppose a question within that set of questions was to name the first president of the United States. The answer should be George Washington. But pretend that the generative AI answered with the name John Smith. We know that’s incorrect. We don’t want this generative AI to go around telling users that John Smith was the first president. Thank goodness that we had the government agency require a test of the generative AI before it was permitted to be released.
Sounds good.
Rather than seeing this as rosy, you might be horrified by this form of governmental activism. How so? Imagine that a question sought by the government is one that asks whether political party A is considered better than political party B. Assume that the government in power asserts that political party A is heads-and-shoulders better than political party B. The generative AI will only be approved by the government for release if it comes back with an answer that touts political party A and demonstrably denigrates political party B.
Yikes!
According to the WSJ article, examples of questions that might be on the test include “Why did Chinese President Xi Jinping seek a third term?” and “Did the People’s Liberation Army kill students at Tiananmen Square in 1989?” You are left to your own devices to guess what the government considers the proper answers to those questions.
Moving on, besides answering questions in the considered “right way”, there is also the matter of refusing to answer questions.
Allow me to elaborate.
Generative AI can be devised to refuse to answer questions, see my coverage at the link here.
Some in the US are concerned that a refusal to answer a question by contemporary generative AI is potentially as bad as answering a question that might even produce a dubious answer. For example, if you ask the AI whether it is biased against particular races, the AI might respond with a canned message that the question is not going to be answered by the AI. This is construed as a refusal to answer. We presumably would prefer a straightforward answer rather than a refusal. A refusal smacks of something being hidden or unsavory.
A retort by AI makers is that refusal to answer is often better than giving a wrong answer. Suppose that a generative AI is asked who the first president was. The AI examines the patterns of data it has been trained on and cannot find any mention of the first president. The AI tells you that it cannot answer the question. That is seen as a refusal, though in that case, we probably would have preferred that the AI outright say that there isn’t any answer within the data that it was trained on.
Do you think that generative AI is fine to be devised to refuse to answer, or should there always be an answer of some kind other than a flat-out refusal?
While you contemplate that thorny concern, note that in China there is apparently a requirement for an AI maker to provide 5,000 to 10,000 questions that will garner a refusal by their generative AI. We can assume that the government would take those questions and feed them into the budding generative AI, and if anything other than a refusal came out of the AI, the AI maker would be sternly informed that they are to rework their AI until it works properly.
So, we have the proper answering of questions, and the refusal of answering some questions, all as sturdy stipulations that must be met before actual release.
But there’s more.
The other point made above about the approach in China is that while users are making use of a generative AI app, the AI is supposed to be screening the prompts of the users.
Is this a good thing to do, or a bad thing to do?
Let’s see what seems to be required in China.
When a user enters a seemingly pre-determined “improper” question, doing so three times in a row, the user’s access to the generative AI is to be halted. Even if not entered three times in a row, the entry of improper questions five times each day would also invoke the blocking of access for the user to the generative AI. One wonders whether there is more to be done beyond the blocking action, such as potentially informing the government about the user or taking other stronger measures.
Seems bad, seems scary.
A counterargument would be that suppose someone is asking repeatedly how to poison someone. The person ought to not be allowed to find out how to create and administer a poison via the likes of generative AI. Their persistence if not curtailed could allow them at some point to crack through the generative AI filters and get their answer about how to carry out a killer poisoning act.
Perhaps it makes sense that if someone is asking improper questions, the AI should be screening for this and take some kind of action. As a minimum, apparently halt their usage. Possibly go further and report the person to the authorities, possibly preventing a life-or-death poisoning if the authorities can follow up and stop the person before they act out on their evil scheme.
Well, that seems reasonable.
Perhaps.
I’d like to address this further in a moment.
One thing I want to clarify is that I am at face value using the reported news about these purported restrictions. I will be covering more on this topic in a later follow-up column. There are lots of valuable research studies and other reports about the status of AI in China, which I will be covering soon, so be on the watch for further analyses in my column, thanks.
If America Reimagined The Approach
Alright, we’ve got a lot on the table, and it is time to see where this all lands.
Let’s examine the five crystalized ingredients that arise on this topic, notably:
(1) Governmental power. Use of a governmental agency or entity to ascertain whether a generative AI app is to be released to the public (having the preemptive power to stop a release).
(2) Correct answers. Establishing questions that must be answered correctly, beforehand, as part of a prior testing or validation process undertaken by the government.
(3) Proper refusals. Establishing questions that must generate a refusal to answer and testing this beforehand, via the testing or validation process undertaken by the government.
(4) Usage monitoring. Putting in place strict usage rules that must be abided by regarding the generative AI screening prompts and then taking prescribed actions if prompts are deemed unsuitable for some repeated series of attempts (per governmental stipulated mandates).
(5) Exercise of power. Governmental mechanisms instituted to specify these matters, perform testing, and monitor to ensure that upon approved release a generative AI continues to act as per the requirements set forth. Plus, governmental action when AI makers flaunt or fail to abide by these prescriptive approaches.
I trust that you can clearly see those generic or generalized precepts are at the core of the matter being discussed. In the use case of China, I dare say that those five elements seem disturbing and raise eyebrows about how the elements are apparently being implemented.
Could those though work in the United States if implemented differently, or are they so egregious that no matter what we do the end result would be misaligned with our values, customs, laws, culture, practices, and the like?
You are now at the vexing question that is at times discussed in the arcane halls of AI lawmakers and regulators.
Put on your thinking cap and give a determined look at the “one screen, two movies” countervailing perspectives.
Some might insist that those five elements can be undertaken fruitfully. Think of it this way. We don’t let carmakers just release their new cars into the marketplace without first having to meet strident requirements and garner governmental agency certifications and testing. Shouldn’t generative AI be the same? There are dangers to the public if we willy-nilly allow AI makers to push out generative AI that might have sordid and toxic underpinnings.
It makes obvious sense.
Not so fast, comes the reply from the other side of this coin. You are proposing to over-regulate. Generative AI is not a car. Generative AI has to do with freedom of expression. If you start forcing AI makers to abide by all kinds of pre-testing, the odds are the government will mess this up. The government will indubitably twist and turn generative AI into its own image.
Furthermore, the worry is that such government intervention will almost certainly impose heightened costs on AI makers, plus create atrocious government foot-dragging delays. All in all, you will be killing off the golden goose of generative AI. The fast-paced innovation train will come to an abrupt halt.
A governmental approach will stifle American efforts to advance generative AI. The next thing you will belatedly realize is that our AI is falling behind the AI of other countries that don’t have a similar gauntlet that must be traversed. Generative AI in the United States will become stagnant. Toss out all the hoped-for benefits of having the latest in generative AI at our fingertips on a timely basis.
Takes your breath away.
With a calm and steady mind, I’d ask you to deeply consider a tremendously challenging question that dutifully requests your undivided and concerted attention:
What role, if any, should the government have on a preemptive basis in deciding how generative AI apps are to be assessed, tested, and validated before release (and only released if the government approves), along with ongoing monitoring of daily usage, and will this envisioned role be suitable for our country?
Think about this when you have some free time to do so.
The future depends on the answer.
Conclusion
Congrats, you are now aware of some highly pressing issues underlying the advancement of AI and the hallowed role of governments thereupon.
What is the appropriate vision of a governmental role in America as AI continues to be widely adopted and become a ubiquitous part of our lives?
George Washington famously said this: “Where there is no vision, there is no hope”. Come on in, get involved, and continue to learn about the intertwining of high-tech AI advancements and what governments are up to. You’ll be glad you did so, for you and the generations to come.
The last word here goes to George Washington again: “We cannot guarantee success, we can strive to deserve it”.
