The model did not "suggest" anything. The model generated text. Google, or more specifically some Google employees (likely following incentives that Google management set up), presented the output of the model as if it might be authoritative answers to users' questions. Some users seemingly fall for the framing/hype and take the generated text as if it is authoritative information to be relied upon. This distinction is critical as we face an onslaught of "AI did <blank>" narratives, when the actual responsibility for such situations lies with specific humans relying on "AI" in such ways.
I think this is typical for software engineers (including myself) to view the world like this: this can't be murder, this is just words, you fell for it yourself. After all words alone are gibberish, the meaning arises in the head of the listener depending on how he interprets them.
However in most countries the justice system works differently. If your neighbor gives you this advice and it kills you, what will matter the most is not the authoritativeness of the advice (he's not a food expert, he was just talking) but the intent. If it also turns out that he had an affair with your wife and mentioned he wants to get rid of you, then he'll very likely go to jail. The "he fell for it and took my advice as if it was authoritative information to be relied upon" defense won't work.
Here (hopefully) the intent is missing. However the whole reasoning that advising partly bears no responsibility for texts it "generates" doesn't work very well.
You're not responding to the argument I'm making, but seemingly to other arguments that have been made over things like 4chan trolling that got people to cook their phones in microwaves.
I'm not saying that users are the only responsible parties here. I'm saying that the humans, including both the users and Google ('s managers and employees), are the only parties that can possibly be responsible. The LLM is not responsible. The LLM is incapable of having intent, as far as we currently know.
I've never used Gemini so I don't know how the user interface comes across, but it might be completely appropriate for Google to be facing liability lawsuits over things like this. What doesn't make sense is narratives that push any of this shared blame onto LLMs or "AI" itself, when the real world effects hinge entirely upon how humans choose to attach these algorithms to the real world.
Thanks for clarification, I agree this is a reasonable view of the situation.
Though I also don't really have problems with headlines like "AI tried to kill a man". True LLM can't be held responsible, but so are e.g. animals. If an unleashed dog kills a man, the owner will be responsible but the headlines will be still about the dog.
Then it can be argued that LLMs don't have intent while the dog had, and many more analogies, arguments and nuances will follow. My point is, this headline is still at an acceptable level of accuracy, unlike many other headlines that distort the reality completely.
The key difference is that everyone mostly understands a dog's capabilities. A reader knows there must be a larger context. Even in your simple example you felt the need to elaborate that it was an "unleashed" dog. If I merely say "a dog killed a man", the reader is left grasping for that larger context and defaulting to thinking a human must ultimately be responsible (most dogs are human owned rather than in wild packs). If I say "a bear killed a man", a similar grasping occurs but defaults to assuming the deceased likely got too close to a wild bear.
Society has no such general understanding for LLMs. If we did, we wouldn't have situations like this in the first place, because people wouldn't be expecting them to generate instructions that can be followed verbatim. Instead we get the marketing buzz of anthropomorphized "AI" as if they're super-human intelligent entities capable of conversing as humans do, but also better. And when they inevitably fail to live up to that, the narrative shifts to the need for "alignment" and "safety", which seems to mostly mean adding some words to the short priming prompt to make it avoid outputting some specific type of thing that was found to be problematic (with the definition of problematic generally being the creation a public relations shitstorm while also not creating profit). But us humans, our only benchmark for comparison, develop our sense of wisdom much slower through repeated learning and development stages. You can't simply read a child an essay of any length about how to drive safely and then hand them the keys to the family car.
