Expanding Intelligence
Featured Hegel and Artificial Intelligence: The Possibility of Thinking
Paper Presentation in a Themed Session Cole Fishman
“Where there is the perception of a purposiveness, an intelligence is assumed as its author; required for purpose is thus the concept’s own free concrete existence,” writes Hegel. This paper examines the question of Artificial Intelligence in light of Hegel’s phenomenology of the mind, specifically in his last few chapters of The Science of Logic where Hegel presciently discusses the functions of mechanism versus teleology. In this paper we aim to frame our discussion through the relationship between sentience, consciousness, and thinking. Part of our framework is that "thinking," using Hegel’s model, is distinct from, and the succession of, these other two categories. The question of whether AI can “think,” then, is best discussed not in light of technology, but as a continuation of the perennial philosophical question which has taken on many different faces: Do animals think? Does the planet think? Does God think? And now, does AI think? Using Hegel as a backdrop we discuss thinking when not using simple human consciousness as the qualifying example. A non-anthropocentric essence to thinking is the subject of our research.
AI-Powered Analysis of Depression Information Preferences: Rhetorical Principles on Question-and-answer Community View Digital Media
Paper Presentation in a Themed Session Yong Jeong Yi, Beom Bae, GyeongCheol Shin, Sojeong Bae, Hyunwoo Moon, June Yoon, Sang Hyuk Lee
The study aims to understand consumer preferences for information about depression in South Korea, using the online Q&A platform Knowledge-iN on Naver for analysis. It explores this through the lens of persuasion, focusing on ethos (credibility), pathos (emotional appeal), and logos (logical appeal). The methodology involves comparing the most upvoted answers to less upvoted ones on the same questions, analyzing the differences in the use of rhetorical principles. The study involves collecting 248 sets of answers (most upvoted and less upvoted) from the platform, with 164 sets used for training and 84 for prediction by a large language model (LLM), specifically ChatGPT 3.5. Researchers initially code each sentence of the answers for rhetorical principles, which then assists in fine-tuning the LLM. The goal is to enhance the model's ability to identify these rhetorical elements automatically using the LLM. The research will evaluate the effectiveness of fine-tuning on the model's predictive capabilities and its impact on improving F1 scores compared to the base model. Additionally, it seeks to compare the analysis of content by researchers and the LLM, to determine the model's accuracy in identifying persuasive elements based on Aristotle's Rhetoric. The study's significance lies in its potential to revolutionize efficiency in humanities and social science research by automating the analysis of persuasive content, traditionally a labor-intensive process. It also contributes to advancements in AI text extraction, highlighting the practical applications of LLMs in understanding consumer preferences and effective health message dissemination.
Featured Beyond the “Yes, but” Syndrome: Evaluating Challenges Faced by Dutch Journalists, Educators, and Students in Implementing AI Technology View Digital Media
Paper Presentation in a Themed Session Jessy De Cooker
In contemporary journalism, artificial intelligence (AI) is becoming a more and more utilized force. Previous studies show that this highlights existing inequalities within media landscapes and within media organisations, where innovation processes are challenged by a lack of resources, risk of failure but also a “yes, but” syndrome (YBS) among (future) journalists. In this study, through a mixed-method approach that combines both semi-structured interviews with journalists from 28 national and regional Dutch news media and surveys among regional journalists, journalism educators and journalism students, I describe the hurdles and obstacles to the inclusion and acceptance of AI methods. The results highlight a discrepancy between attitudes and actions among (future) journalists in relation to AI use on four levels: a lack of AI literacy, a high degree of pointing to external factors for not employing AI tools or methods, a self-sense of ineptitude towards structurally employing AI in a professional journalistic setting, and the unavailability to acquire sufficient AI knowledge themselves or offered by their media organisations. On all levels, an overall lack of awareness about the impact of AI is evident among inexperienced journalists as well as AI frontrunners. My research contributes not only to the ongoing discourse on AI integration, human-machine perceptions and participation among AI divides within Dutch journalism, but also sheds new light on the challenges and opportunities inherent in shaping a responsible AI-driven journalistic practice.
