Computers can make decisions: Almost everyone reading this will have a implemented an if-else statement or equivalent control flow. That is a decision made by a computer on data it receives at runtime. You can argue that it's different, because we fully understand the logic of such a statement, whereas LLMs are closer to a black-box. But at Turing observed, in practice we are frequently surprised by the output of complex algorithms, even those we have authored ourselves. The more difficult the task, the more complex the algorithm we need to achieve it, and the more unexpected behaviour can emerge from it. In that way, LLMs are just the latest, highest point on a rising line of algorithmic capability and complexity.
We can (and should) try to improve AI safety and alignment, but perfect safety is probably impossible without losing the capabilities that make AI unique. Black-and-white positions that insist anyone creating or using an AI is responsible for anything it does wrong under any circumstances are also unrealistic. AI is a general purpose technology, and we have always accepted that it is not practical for suppliers to envisage and constrain every downstream use: Car manufacturers cannot prevent every instance of mechanical failure, nor can they stop cars being used in crime.
We can (and should) try to improve AI safety and alignment, but perfect safety is probably impossible without losing the capabilities that make AI unique. Black-and-white positions that insist anyone creating or using an AI is responsible for anything it does wrong under any circumstances are also unrealistic. AI is a general purpose technology, and we have always accepted that it is not practical for suppliers to envisage and constrain every downstream use: Car manufacturers cannot prevent every instance of mechanical failure, nor can they stop cars being used in crime.