AI and Common Sense

Common sense is the endless frontier in the development of artificial intelligence, but what exactly is common sense, can we replicate it in algorithmic form, and if we can - should we? Bauer, Schiele and their contributors from a range of disciplines analyse the nature of common sense, and the consequent challenges of incorporating into artificial intelligence models. They look at different ways we might understand common sense and which of these ways are simulated within computer algorithms. These include sensory integration, self-evident truths, rhetorical common places, and mutuality and intentionality of actors within a moral community. How far are these possible features within and of machines? Approaching from a range of perspectives including Sociology, Political Science, Media and Culture, Psychology and Computer Science, the contributors lay out key questions, practical challenges and "common sense" concerns underlying the incorporation of common sense within machine learning algorithms for simulating intelligence, socialising robots, self-driving vehicles, personnel selection, reading, automatic text analysis, and text production. A valuable resource for students and scholars of Science-Technology-Society Studies, Sociologists, Psychologists, Media and Culture Studies, human-computer interaction with an interest in the post-human, and programmers tackling the contextual questions of machine learning.

Martin W. Bauer is Professor of Social Psychology and Research Methodology at the London School of Economics and Political Science (LSE). He investigates "common sense" in relation to science and emerging technologies in the international MACAS (Mapping the Cultural Authority of Science) network. He is a Fellow of the German National Academy of Technical Sciences (acatech). Recent publications include The Psychology of Social Influence: Modes and Modalities of Shifting Common Sense (2021, with Gordon Sammut); Atom, Bytes & Genes: Public Resistance and Techno-scientific Responses (2015). Bernard Schiele (PhD) is a Professor of Communications in the Faculty of Communication at the University of Québec at Montréal (Canada). He has been working for a number of years on the socio-dissemination of S&T. Among other books he has recently published are Science Communication Today (2015, with Joëlle Le Marec and Patrick Baranger); Communicating Science, A Global Perspective (2020 with Toss Gascoigne and colleagues); Science Culture in a Diverse World: Knowing, Sharing, Caring (2021, with Xuan Liu and Martin Bauer); Le musée dans la société [The Museum in Society] (2021), and Science Communication: Taking a Step Back to Move Forward (2023, with Martin Bauer).

Introductory Comment When AI meets common sense, frictions will arise Part 1: The scene and the argument of common sense 1. AI with common sense: What concept of common sense? 2. Self-awareness and common sense: The paradox of AI. A dispassionate look Part 2: Egocentric common sense: AI with additional features 3. Giving AI some common sense 4. Human interaction with robots 5. Towards robots with common sense 6. Common sense, artificial intelligence and psychology Part 3: Inter-subjective common sense: public discourse 7. Giambatistta Vico's dialogical common sense 8. The a-sociability of AI: Knowledge, social interactions, and the dynamics of common sense 9. Exploring the common wisdom on artificial intelligence and its political consequences: The case of Germany 10. Associations of AI and common sense in the news 11. Meanwhile in Japan: The possibility of Techno-animism for engaging deliberation for emerging technology Part 4: Unsettling or highlighting common sense? 12. Common-sense attributions of AI agency: Evidence from an experiment with ChatGPT 13. The challenges and opportunities in large language models: Navigating the perils of stochastic and scholastic parrots in artificial understanding and common sense 14. Artificial intelligence in personnel selection: Reactions of researchers, practitioners and applicants 15. Self-driving vehicles (SDVs) and common sense Part 5: Conclusion 16. AI goes to the movies: Fast, intermediate and slow common sense