Longin, Louis (2023): The social role of AI advisers. Dissertation, LMU München: Faculty of Philosophy, Philosophy of Science and the Study of Religion
This book examines the ontological status of AI advisers, arguing that they occupy a unique conceptual space between tools and human agents in terms of their agentive capacities, responsibility attribution, and ability to influence human perception and decision-making.
Longin, L., Briceno, D. M., & Poquet, O. (2024). The Power of Conversation: Learners Become More Cautious Sharing Learning Data after a Group Discussion. Zenodo. https://doi.org/10.5281/zenodo.13294013
Learners become more cautious about sharing their learning data after participating in a group discussion, with the willingness to share being lowest when data is submitted to a government entity and for a collective benefit.
Deroy, O., Longin, L., & Bahrami, B. (2023). Co‐perceiving: Bringing the social into perception. Wiley Interdisciplinary Reviews: Cognitive Science, e1681.
The paper introduces the concept of co-perception as the ability to distinguish between objects perceived privately and those perceived commonly with others.
Longin, L., Bahrami, B., & Deroy, O. (2023). Intelligence brings responsibility-Even smart AI assistants are held responsible. iScience, 26(8).
Even basic AI assistants are perceived as sharing responsibility with their human users for outcomes, despite being viewed as tools, while non-AI tools are not held responsible.
Longin, L., & Deroy, O. (2022). Augmenting perception: How artificial intelligence transforms sensory substitution. Consciousness and Cognition, 99, 103280.
The paper discusses how artificial intelligence transforms sensory substitution and augmentation devices by introducing non-linear signal processing, which allows for more efficient and effective sensory information transfer, potentially reducing cognitive overload and improving usability for users.
Longin, L. (2020). Towards a Middle-Ground Theory of Agency for Artificial Intelligence. In Culturally Sustainable Social Robotics (pp. 17-26). IOS Press.
The paper proposes a middle-ground theory of agency for artificial intelligence that balances between attributing too much or too little agency to AI systems