As artificial intelligence (AI) advances, its role in various industries becomes increasingly significant. This has led to a growing debate about the role of human judgment in creating synthetically authored papers, synthetically drawn works or other creative outputs. In this essay, I will explore the concept of augmented authorship, where AI acts as a copilot rather than an author, and how AI can assist professionals rather than compete with them. I will also discuss the challenges and opportunities of AI in different industries, drawing inspiration from Time Magazine’s list of the 100 most influential people in AI (Time, 2023).
Augmented Authorship: AI as a Copilot
AI has the potential to transform the way professionals work across various industries. For instance, Kate Crawford, a Professor at USC Annenberg School and founder of the Knowing Machines project, has studied how large-scale data systems have impacted the environment and how they have impacted our social and political systems for the past 20 years (Time, 2023). Her work highlights the importance of human judgment in understanding the ethical implications of AI in content creation. In the field of AI-assisted programming, Stuart Russell, a Professor of computer sciences at the University of California, Berkeley, has made foundational contributions to the development of AI (Time, 2023). His work on AI safety research and the development of AI models demonstrates the importance of human judgment in guiding AI development and ensuring its responsible use.
AI assisting professionals: Collaboration and enhancement
AI can be used to enhance the work of professionals in various fields. For example, Charlie Brooker, the creator of the television series Black Mirror, has explored the potential impact of AI on storytelling and content creation (Time, 2023). His work highlights the importance of human judgment in shaping narratives and the ethical considerations that arise when AI is used in creative processes. In the field of research, the Allen AI Predoctoral Young Investigator program in the US offers predoctoral candidates the opportunity to prepare for graduate-level research through partnership with strong mentors, participation in cutting-edge research, and co-authorship of papers at top venues. This program exemplifies how AI can augment the work of researchers and foster collaboration.
Challenges and Opportunities of AI in different industries
AI presents various challenges and opportunities for professionals in different industries. For example, in healthcare, AI can improve diagnostics, treatment planning, and personalised medicine (Liu et al., 2021). In finance, AI can help detect fraud, optimise trading strategies, and enhance customer service (Chen et al., 2021). AI can improve crop monitoring, yield prediction, and resource management (Kamilaris et al., 2017). However, there are also potential challenges associated with AI in these industries. Ethical considerations, job disruption, and data privacy remain critical challenges (Chiang, 2023). Additionally, the increasing use of AI in content creation may lead to ethical concerns, such as the potential for misuse (Baker et al., 2019).
The Role of AI in education and learning design
AI has the potential to revolutionise education and learning design by providing personalised learning experiences, automating administrative tasks, and offering new ways to engage students (Luckin et al., 2016). For instance, AI-powered adaptive learning systems can tailor educational content to individual students’ needs, helping them progress at their own pace and maximising their learning outcomes (Woolf, 2010). Furthermore, AI can assist educators in identifying students who may be struggling and provide targeted interventions to support their learning (Zhang et al., 2017). However, integrating AI in education also raises concerns about data privacy, algorithmic bias, and the potential loss of human interaction in learning (Selwyn, 2019).
The Future of AI and human collaboration
As AI advances, it is crucial to consider how human judgment and collaboration can be integrated into AI systems to ensure ethical and responsible use. One approach is to develop AI systems that are transparent, explainable, and accountable, allowing users to understand the underlying decision-making processes and challenge any potential biases or inaccuracies (Arrieta et al., 2020). Another approach is to promote interdisciplinary collaboration between AI researchers, domain experts, and stakeholders to ensure that AI systems are designed with a deep understanding of the specific contexts and challenges they are meant to address (Holm, 2019).
In conclusion, AI has the potential to revolutionise the way professionals work and collaborate. By acting as a copilot, AI can augment the work of professionals and enhance collaboration. However, it is essential to consider the challenges and opportunities of AI in different industries, including ethical concerns, job disruption, and data privacy. As AI continues to advance, professionals, researchers, and policymakers must work together to harness the potential of AI while addressing its challenges and ensuring that human judgment remains at the core of the creative process.
References
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., … & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115.
Baker, R. S., Merceron, A., & Pavlik Jr, P. I. (2019). Educational data mining and learning analytics. In Handbook of Educational Psychology (pp. 1-22). Routledge.
Chen, X., Li, Y., & Zhang, X. (2021). Artificial intelligence in finance: A review. Journal of Finance Research, 2(2), 1-14.
Chiang, T. (2023). Will A.I. Become the New McKinsey? The New Yorker. Retrieved September 10, 2023, from https://www.newyorker.com/science/annals-of-artificial-intelligence/will-ai-become-the-new-mckinsey
Holm, E. A. (2019). Artificial intelligence in materials science: The present and future role of human–AI collaboration. MRS Bulletin, 44(9), 676-681.
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37.
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., … & Keane, P. A. (2021). A comparison of deep learning performance against healthcare professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health, 3(6), e350-e357.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity.
Time. (2023). The 100 Most Influential People in AI. Retrieved September 10, 2023, from https://time.com/collection/time100-ai/
Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionising e-learning. Morgan Kaufmann.
Zhang, M., Trnavac, R., Luo, W., & King, I. (2017). Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on World Wide Web (pp. 765-774).
AI Author Statement
This article was developed using a combination of human expertise and artificial intelligence (AI) tools. Up to 40% of the work was made through stepped prompting, which involved providing the AI with specific questions and information to generate relevant content. The online research was conducted through AI using the TIME 100 AI list as a starting point (Time, 2023). The AI-assisted technologies used in the writing process were employed to enhance the efficiency and quality of the content. However, as the author, I bear ultimate responsibility for the accuracy, appropriateness, and originality of the text in this article.
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