‘ChatGPT et al.’: The ethics of using (generative) artificial intelligence in research and science

Artificial intelligence (AI) seeks to make computers do what human minds can do. By ‘AI’, we refer to the use of machine learning, algorithms, large datasets, neural networks and traditional statistical reasoning by computing. The term ‘AI’ is misleading: Despite suggestions to the contrary (Bubeck et al., 2023) – and some surely impressive achievements in specific areas –we are still far from reaching the benchmark of ‘general human intelligence’. AI has undergone several generations, from ‘good old-fashioned AI’ (Haugeland, 1989), the defined algorithms of which failed at the common sense problem, to the current and more successful generation of neural network and deep learning AI. One specific form of current AI is ‘generative AI’ (e.g. ChatGPT, DALL-E, Midjourney) – and without a doubt, it’s the ‘technology hype’ of 2023 and the focus of this editorial comment. Generative AI, specifically ChatGPT, became a ‘cultural sensation’ (Thorp, 2023) rather rapidly in early 2023. When Daniel brought up generative AI as a future ethical issue at a panel for journal editors on publishing ethics in December 2022 (Burton-Jones et al., 2022), many audience members seemed unfamiliar withMidjourney orChatGPT.However, within just a few weeks, the landscape shifted dramatically. Publicly launched on 30 November 2022, ChatGPT – a chatbot built on top of a text-generating AI – had an impressive debut, reaching onemillion users within 5 days and surpassing 100million users in January 2023 (Dwivedi et al., 2023). Since then, ChatGPT has become widely used and is believed to impact many areas, including research and science (Hill-Yardin et al., 2023; Liebrenz et al., 2023; Lund and Wang, 2023). While detailed explanations of the underlying technology can be found in other sources (Goodfellow et al., 2016), generative AI is a subset of deep learning AI that specialises in producing human-like outputs. OpenAI’s ChatGPToperates on a neural network AI architecture, GPT (Generative Pretrained Transformer). Although ChatGPT might have seemed like a natural progression of the AI domain, especially since Midjourney and DALL-E had been introduced earlier, it astonished global audiences and led companies like Alphabet (Google) to hastily release comparable tools (Teubner et al., 2023). Simplified, deep learning AI systems ‘hallucinate’ ‘plausible looking’ (though not necessarily accurate) responses to user prompts. They base these responses on patterns of ‘likeness’ (associations between words and concepts), stored in a digital neural network (multiple layers of interconnected nodes) and learnt from massive training datasets. Such systems can quickly generate high-quality images and texts, outperforming traditional algorithms. However, this advanced capability is accompanied by the challenge of the ‘black box’ problem:wemay understand the model’s general principles, but the reasons behind specific decisions remain opaque. The neural network provides a flexible, changing structure, inspired by the human brain, that encodes patterns, but not in an intelligible, auditable manner – there is no clear formula to scrutinise. (This is akin to how the reader might instantly and reliably distinguish between their mother and their cat but would be unable to write down a precise formula for this recognition process). As journal editors, the emergence of ChatGPT prompted us – and others (e.g. Hill-Yardin et al., 2023; Liebrenz et al., 2023; Lund and Wang, 2023; Teubner et al., 2023; Van Dis et al., 2023) – to ask foundational questions about using generative AI in research and science. Specifically: Is it ‘ethical’ to use generative or other AIs in conducting research or for writing academic research papers? In this editorial, we go back to first principles to reflect on the fundamental ethics to apply to using ChatGPT and AI in research and science. Next, we caution that (generative) AI is also at the ‘peak of inflated (hype) expectations’ and discuss eight in-principle issues that AI struggles with, both ethically and practically. We conclude with what this all means for the ethics of using generative AI in research and science.