Of course, there is no reason we should expect more from our creations than we do from their creators: we can draw a parallel here with human reasoning. Often, the reasons we give for a decision are a post hoc justification and not the real reason at all. Bias goes undetected in human decision making all the time, even to the decision maker, and an AI could behave the same way. Who takes responsibility for the decision made by a biased staff member? Normally, it is the organisation, not the individual. So must it be for AI decision making.

Our examples so far reflect decision making and the ethical risks of leaving that to machines. What happens when machines output human languages? In May of this year, a particular kind of natural language processing model, GPT-3 hit the news. Developed by a private company called OpenAI it was released in a restricted way for research purposes. Its creators openly admitted that there were many potential use cases that were unexplored and possibly harmful and some of the early output confirmed their fears, showing leanings towards antisemitism and gender bias. That was unsurprising since much of the training text came from the uncensored web where these views are often shared under the cover of anonymity. GPT-3 is the latest in a series of models trained on ever larger corpora of the written word but differs from its smaller ancestors in that it is too large to be trained outside of the lab environment. Instead, it is provided in its trained state and simply prompted by the user with phrases, descriptions or requests. It then outputs some paragraphs of related text, which could be answers to questions asked, an interview or screenplay, the beginnings of a story or even an entire article on the subject prompted. Some of the output will be verbatim from the gigabytes of documents (including Wikipedia articles and scientific papers) but much is strung together from word and phrase associations in the training data and resembles original thought. Controversy is widespread, much of it related to how ‘human’ the output seems at first glance. Much of its writing is of a higher standard than the clickbait articles that have become so familiar in the advertisement funding model of the modern web. GPT-3 responses to human questions even went unnoticed on Reddit for a week, even though some topics were very sensitive.^[1] There are concerns about the sheer rate of unedited output which is possible, although the developers in collaboration with Microsoft are reviewing each new use case to filter out the likely worst abusers. Even with access strictly controlled, it will not be long before GPT-3 and other competing technologies are widely accessible to whoever should want to use them, regardless of their goals. Misinformation campaigns, spam email content, SEO filler text: all are now inexpensively in reach with human input reduced to editing the junk rather than acting creatively to produce it.