LLM Chatbots: Panacea or Pandora’s Box?

ChatGPT, for a Special Issue we want to address the following question: ‘LLM chatbots: Panacea or Pandora's Box?’ How do you answer this question?

The question of whether LLM (Language Model-based) chatbots are a panacea or a Pandora's box is an intriguing one. Let us explore both perspectives:

Panacea:

1. Enhanced Communication: LLM chatbots have the potential to revolutionize communication by providing quick and accurate responses across various domains. They can handle multiple languages, understand context, and offer personalized interactions.
2. Efficiency and Convenience: Chatbots can handle numerous queries simultaneously, reducing response time and improving efficiency. They can automate repetitive tasks, freeing up human agents to focus on more complex and value-added activities.
3. Scalability: LLM chatbots can scale effortlessly to handle large volumes of inquiries, ensuring consistent service quality. They are available 24/7, enabling businesses to provide round-the-clock support to their customers.
4. Cost Savings: By automating customer interactions, organizations can potentially reduce costs associated with customer service and support functions. This is particularly beneficial for smaller businesses or those with limited resources.

Pandora's Box:

1. Ethical Concerns: The capabilities of LLM chatbots raise ethical considerations regarding data privacy, transparency, and potential misuse. They require extensive data for training, which may lead to privacy concerns if not handled carefully.
2. Bias and Discrimination: Chatbots can inadvertently perpetuate biases present in the data they are trained on, leading to discriminatory responses or actions. This can have negative social implications and contribute to inequality and injustice.
3. Loss of Human Touch: While chatbots can provide efficient responses, they lack the human touch and empathy that some individuals desire. In emotionally charged situations, customers may prefer interacting with human agents who can better understand their needs.
4. Technical Limitations: LLM chatbots are not infallible and can encounter difficulties when faced with complex or ambiguous queries. They may struggle to understand context, humor, or nuanced conversations, leading to frustrating user experiences.

Ultimately, whether LLM chatbots are a panacea or a Pandora's box depends on how they are developed, deployed, and regulated. By addressing ethical concerns, ensuring transparency, and focusing on continuous improvement, LLM chatbots can be valuable tools that enhance efficiency and customer experiences. However, caution must be exercised to prevent potential negative consequences and ensure a balanced approach to their implementation. 

26.05.2023 - Free Research Preview. ChatGPT may produce inaccurate information about people, places, or facts. ChatGPT May 24 Version

Dear Colleagues,

ChatGPT, a Large Language Model chatbot, generated the above answer when prompted to discuss whether LLM chatbots should be considered a Panacae or a Pandora’s Box. For our Special Issue, we are seeking contributions from human experts focusing on this topic. 

With their rapid integration and application across various sectors and in everyday life, LLM chatbots have triggered both optimistic and pessimistic responses. This Special Issue seeks to collect a selection of papers that critically explore the promise and peril of LLM chatbots from a variety of different fields, approaches and perspectives (e.g., governance, political economy, technology and usage). We welcome research articles, conceptual papers and reviews positioned within a specific field, such as education, science, journalism, medicine, technology, etc. Potential topics may include:

(1) The social, ethical, epistemological or environmental impact of using LLM chatbots such as ChatGPT  (Van Es et al, 2021), including its opportunities and threats in terms of its ability to extend human capabilities (Lawson, 2010).
(2) Ethical issues (Grunwald, 2014, Jonas, 1973), e.g., with regard to its training data (see https://www.washingtonpost.com/technology/interactive/2023/ai-chatbot-learning/) exploring issues of bias/stereotyping, content moderation policies.
(3) Accountability and regulation of generative AI.
(4) The characteristics of chatting with LLM chatbots (Champagne, 2023), e.g., anthropomorphism.
(5) The reliability of LLM chatbots’ answers (see Dumitru et al. (2022) and Loos & Ivan (2022) for discussions on accessible reliable digital information and Guo al.’s (2023) work on LLM chatbots versus human expertise) compared to those of other LLM chatbots, such as Bard, Perplexity AI and Neuroflash (see also https://aibusiness.com/nlp/7-language-models-you-need-to-know
(6) Media discourses around LLM chatbots, e.g., (Nguyen and Hekman, 2022) or their public perception (Brauner et al., 2023). 

References

Brauner, P., Hick, A., Philipsen, R., & Ziefle, M. (2023). What does the public think about artificial intelligence?—A criticality map to understand bias in the public perception of AI. Frontiers in Computer Science, 5, 19.

Champagne, M. (2023). Chatting with an AI, Chatting with a Human, What’s the Difference? Conference presentation. Conference: Philosophers’ Jam, Vancouver, Canada. file:///E:/Onderzoek/ChatGPT/MarcChampagneLangara2023.pdf

Dumitru, E.-A., Ivan, L., & Loos, E. (2022). A Generational Approach to Fight Fake News: In Search of Effective Media Literacy Training and Interventions. In Q. Gao & J. Zhou (Eds.), Human aspects of IT for the aged population, In International Conference on Human–computer Interaction, HCII 2022, LNCS series. Springer International Publishing. https://0-link-springer-com.brum.beds.ac.uk/chapter/10.1007/978-3-031-05581-2_22

Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., ... & Wu, Y. (2023). How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection. arXiv preprint arXiv:2301.07597.

Grunwald, A. (2014). Technology assessment for responsible innovation. Responsible innovation 1: Innovative solutions for global issues, 15-31.

Jonas, H. (1973). Technology and responsibility: Reflections on the new task of ethics. Social Research, 40, 31-54.

Lawson, C. L. I. V. E. (2010). Technology and the extension of human capabilities. Journal for the theory of social behaviour, 21, 8308.

Loos, E., & Ivan, L. (2022). Special Issue “Fighting Fake News: A Generational Approach.” Societies, 12(2), 57. https://0-www-mdpi-com.brum.beds.ac.uk/journal/societies/special_issues/fake_news

Nguyen, D., & Hekman, E. (2022). The news framing of artificial intelligence: a critical exploration of how media discourses make sense of automation. AI & Soc., DOI: 10.1007/s00146-022-01511-1

van Es, K., Schäfer, M. T., & Wieringa, M. (2021). Tool Criticism and the Computational Turn: A 'Methodological Moment' in Media and Communication Studies, 46-64 Nomos eLibrary, DOI: 10.5771/1615-634X-2021-1-46

Dr. Eugène Loos
Dr. Karin van Es
Dr. Rianne van Lambalgen
Guest Editors

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• large language models chatbots as innovative tool
• ChatGPT
• panacea
• pandora’s box
• opportunities
• threats
• social impact
• anthropomorphism
• human capability
• ethics
• accountability
• regulation