The Future of Authorship: AI Text Generation
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Presented as a guest lecture at Galala University, Egypt (13 December 2021). Provides a gentle introduction to natural language generation, with English- and Arabic-language examples. Offers questions to guide further thought about social and ethical responsibilities related to changing conceptions of authorship. Note that some slides are animated and do not present accurately on SlideShare.
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The Future of Authorship: AI Text Generation
- 1. The Future of Authorship: AI Text Generation Leah Henrickson Lecturer in Digital Media School of Media and Communication University of Leeds, UK L.R.Henrickson@leeds.ac.uk
- 2. First, who am I? • Lecturer in Digital Media, School of Media and Communication, University of Leeds, UK • Programme Leader, MA New Media • Founder and Manager, University of Leeds Sociologies of AI Network • PhD: Social Sciences and Humanities (Loughborough University, UK) • artificial intelligence, digital storytelling, electronic literature, natural language generation • Canadian 🍁
- 4. Authorship is an identity, with particular connotations, engaging in particular activities that result in particular outputs.
- 5. Who is the author? What is the author? What does the author do? What does the author produce?
- 7. Natural language generation (NLG) refers to the computational production of output in everyday human languages.
- 8. Ahmed Fadhil and Ahmed AbuRa’ed, ‘OlloBot – Towards A Text-Based Arabic Health Conversational Agent: Evaluation and Results’, in Proceedings of Recent Advances in Natural Language Processing (Varna: ACL, 2019), pp. 295-303 <https://aclanthology.org/R19-1034.pdf>.
- 10. Sunspring: https://www.youtube.com/watch?v=LY7x2Ihqjmc; script: https://www.thereforefilms.com/uploads/6/5/1/0/6510220/sunspring_final.pdf
- 12. Arnold C. Satterthwait, 'ARABIC TO ENGLISH TRANSLATION’, Quarterly Process Report, 67 (1962), pp. 171-176 <https://aclanthology.org/www.mt-archive.info/50/MIT-RLE-1962-Satterthwait.pdf>. (Thanks to James Ryan for bringing this example to my attention.) Additionally, see: Ahmed H. Alneami, Design and Implementation of an English to Arabic Machine Translation (MEANA MT) [Doctoral Thesis] (Sheffield: University of Sheffield, 1996) <https://etheses.whiterose.ac.uk/14819/1/341826.pdf>.
- 13. From Machine Translation to NLG (Arabic) • Currently very few attempts to develop NLG systems for Arabic • Arabic poses unique grammatical changes • Wael Abed and Ehud Reiter, ‘Arabic NLG Language Functions’, in Proceedings of the 13th International Conference on Natural Language Generation (Dublin: ACL, 2020, pp. 7-14 <https://aclanthology.org/2020.inlg-1.2.pdf>. • But there’s been some (very preliminary) success in summarising input texts! • Sally S. Ismail, Mostafa Aref, and Ibrahim F. Moawad, ‘A Model for Generating Arabic Text from Semantic Representation’, in 11th International Computer Engineering Conference (Cairo: IEEE, 2015), pp. 117-122 <https://ieeexplore.ieee.org/document/7416335>. • Molham Al-Maleh and Said Desouki, ‘Arabic Text Summarization Using Deep Learning Approach’, Journal of Big Data, 7:109 (2020) <https://doi.org/10.1186/s40537-020-00386-7>. • Hani D. Hejazi, Ahmed A. Khamees, Muhammad Alshurideh, Said A. Salloum, ‘Arabic Text Generation: Deep Learning for Poetry Synthesis’ in International Conference on Advanced Machine Learning Technologies and Applications (Cham: Springer, 2021), pp. 104-116.
- 14. AraGPT2 ‘AraGPT2-Mega successfully fooled approx. 60% of the respondents, with longer passages having a higher error rate than short passages. In the provided explanations, some subjects relied on punctuation mistakes, coherence, and repetition issues, with others spotted factual inaccuracies. However, the results also show that humans were misclassifying human-written 50% the time (chance level performance), while also citing factual inconsistencies, grammatical errors, and unusual writing styles [all sic].’ Wissam Antoun, Fady Baly, and Hazem Hajj, ‘AraGPT2: Pre-Trained Transformer for Arabic Language Generation, arXiv (v2, 2021), p. 4 <https://arxiv.org/abs/2012.15520>.
- 15. AraGPT2 ‘AraGPT2-Mega successfully fooled approx. 60% of the respondents, with longer passages having a higher error rate than short passages. In the provided explanations, some subjects relied on punctuation mistakes, coherence, and repetition issues, with others spotted factual inaccuracies. However, the results also show that humans were misclassifying human-written 50% the time (chance level performance), while also citing factual inconsistencies, grammatical errors, and unusual writing styles [all sic].’ Wissam Antoun, Fady Baly, and Hazem Hajj, ‘AraGPT2: Pre-Trained Transformer for Arabic Language Generation, arXiv (v2, 2021), p. 4 <https://arxiv.org/abs/2012.15520>. In short, people are bad at telling the difference between human-written and computer-generated texts.
- 16. Wissam Antoun, Fady Baly, and Hazem Hajj, ‘AraGPT2: Pre-Trained Transformer for Arabic Language Generation, arXiv (v2, 2021), p. 9 <https://arxiv.org/abs/2012.15520> (random unseen contexts about children’s stories).
- 17. Wissam Antoun, Fady Baly, and Hazem Hajj, ‘AraGPT2: Pre-Trained Transformer for Arabic Language Generation, arXiv (v2, 2021), p. 8 <https://arxiv.org/abs/2012.15520> (random unseen context about coronavirus vaccine).
- 19. AI reconfigures how we consider the role and responsibilities of the author or artist. Prominent researchers of AI and digital narrative identity D. Fox Harrell and Jichen Zhu wrote in 2012 that the discursive aspect of AI (such as applying intentionality through words like ‘knows,’ ‘resists,’ ‘frustration,’ and ‘personality’ is an often neglected but equally pertinent aspect as the technical underpinnings. ‘As part of a feedback loop, users’ collective experiences with intentional systems will shape our society’s dominant view of intentionality and intelligence, which in turn may be incorporated by AI researchers into their evolving formal definition of the key intentional terms.’ Drew Zeiba, ‘How Collaborating With Artificial Intelligence Could Help Writers of the Future: On the Growing Potential of Computational Literature’, Literary Hub (9 November 2021) <https://lithub.com/how-collaborating-with-artificial-intelligence-could-help-writers-of-the-future>. Additionally, see: Matthew Kirschenbaum, ‘Spec Acts: Reading Form in Recurrent Neural Networks’, ELH, 88.2 (2021), 361-386.
- 21. You read stories arguably for, like, the human touch to it, to understand people or to understand a person’s frame of mind. You don’t want to understand a robot’s frame of mind. Because it’s kind of like, a robot will always – well, I don’t know if always – it’s always kind of collating what’s already there, and just trying to relay it. Focus Group Participant Response (2017-2018)
- 22. We humans think we are always interested in how something was made. Like, even if we’re like, ‘okay, who is this writer? What is his biography? Which place from Earth he comes from? What was happening there? What part of history is he from?’ We always try to understand the text. We never analyse just text, just on its own. We will always put it in context with other things. I guess we do the same with computer text. Focus Group Participant Response Focus Group Participant Response (2017-2018)
- 23. Okay, so as the – in art, the viewer – the artist is the one half of the equation, and the viewer is the other half. The viewer then draws meaning from a poem, and I’m wondering whether some of your feeling cheated is that you are drawing some meaning from this poem, working at an understanding in some way, in the knowledge that somebody else has put some effort into conveying some meaning, and what you receive might be different from what they intended because you are not them, they are not you, and you’ve got your own context and bag of whatever that you carry with you, which means that you see it in a certain way. But if it’s a computer, it’s kind of like we’re all kind of cheated because nobody’s done that other work that we’re doing on the other side of the mirror or the equation or however you want to look at it. The scales aren’t balanced, really, and I think that if we have computer-generated texts, then, well, I just don’t see the need for human beings. Focus Group Participant Response Focus Group Participant Response (2017-2018)
- 24. Ethical Issues with NLG • What can we do with this technology? • What should we do with this technology? • What shouldn’t we do? • Is it really Jessica speaking here? • How might this affect Joshua? Images and Project December example from https://www.sfchronicle.com/projects/2021/jessica-simulation-artificial-intelligence.
- 26. Past: Raymond Queneau’s Hundred Thousand Billion Poems (1961); Present: Grammarly
- 27. Future: Co-creation of ‘synthetic literature’. See: Folgert Karsdorp, Ben Burtenshaw, and Mike Kestemont, ‘Synthetic Literature: Writing Science Fiction in a Co-Creative Process’, in Proceedings of the INLG 2017 Workshop on Computational Creativity and Natural Language Generation (Santiago de Compostela: Association for Computational Linguistics, 2017), pp. 29-37.
- 28. Who is the author? What is the author? What does the author do? What does the author produce?
- 29. جزيال شكرا Leah Henrickson Lecturer in Digital Media School of Media and Communication University of Leeds, UK L.R.Henrickson@leeds.ac.uk
Editor's Notes
- In this talk, I’ll be focusing on the social sides of NLG and AI text technologies. I’m a social scientist, not a computer scientist! However, I will provide some computer science references throughout the talk for those of you who would like to explore these issues more. First, I’ll set the stage with a review of what I mean by authorship. Then, I’ll give you some examples of NLG systems that you might come across in your everyday activity. I’ll also give you some examples of Arabic NLG systems in particular. There are only a few Arabic NLG systems out there, so any developers in the room might look to doing some work in this area. After I’ve shown you some examples of NLG systems, I’ll review how people are talking about these systems, both in the news and in a series of focus groups that I ran a few years ago. As you’ll see, people don’t just accept this technology. They have questions and reasonable concerns about how NLG systems can support human activity. I’ll then encourage you to think about ethics of NLG and AI-authored technologies. What can we do? What should we do? Finally, I’ll wrap up with some questions to guide your thinking about the future of authorship and AI text generation, and then we’ll have some time for questions and comments.
- Chatbots are often template-based – they’re actually pretty simple technology. But they come from a long lineage. Think, for example, about Turing’s ‘imitation game’/’Turing test’ (1936), ELIZA (1960s), the Loebner Prize, and so forth.
- Here, Narrative Science has been listed as a ‘partner’ in the byline, but the reader is still not told that this a computer-generated texts. In other instances, no author/partner is listed. How often do you read computer-generated texts without even knowing it?
- *How is the computer producing this sentence?
- This quotation emphasises the importance of understanding not just how the technology works, but also how we talk about that technology. Everyday people don’t know the ins and outs of NLG or AI, and depend upon descriptions that actually make ample use of metaphor that directs their thought. What are the implications of anthropomorphising AI systems in the ways noted here? Is output from AI systems wholly comparable to output from human producers? Why or why not? These are the kinds of questions I’ve been exploring in my own work about attributing agency to NLG systems. In my article in Digital Creativity, I argue that we can and should attribute agency to NLG systems to explicitly recognise the communicative influence of these systems’ texts in our lives. Do you agree? Do you disagree?
- As has been indicated by the news articles shown a few slides ago, conceptions of NLG often tend towards dystopia or utopia – rather than offer nuanced considerations of the technology’s potential and problems, arguments are usually pretty polarising. I believe there are both good and bad things about NLG, but let’s see what everyday people said when I asked them about their feelings in a series of focus groups that I ran a few years ago.
- Focus group participants tended to be uncomfortable with the idea of NLG systems and their resultant computer-generated texts. This discomfort appeared to stem from the perception of text as a tool for communication that contributes to the development of interpersonal understanding and relationships.
- Here’s the good stuff! While this presentation has focused on the issues with NLG, here are plenty of positive applications of this technology. We’ve already done so much, and there’s lots of room for growth.
- I’m not going to provide you with any answers here – just questions to get you thinking about the social implications of NLG systems (in their various forms). Whether you go into developing NLG systems, or just continue to think critically about those systems’ output, the future of AI authorship starts with you. Really, the future starts here.