Can AI mediation improve democratic deliberation?

arXiv (Cornell University) · 2026-01-09

The strength of democracy lies in the free and equal exchange of diverse viewpoints. Living up to this ideal at scale faces inherent tensions: broad participation, meaningful deliberation, and political equality often trade off with one another (Fishkin, 2011). We ask whether and how artificial intelligence (AI) could help navigate this "trilemma" by engaging with a recent example of a large language model (LLM)-based system designed to help people with diverse viewpoints find common ground (Tessler, Bakker, et al., 2024). Here, we explore the implications of the introduction of LLMs into deliberation augmentation tools, examining their potential to enhance participation through scalability, improve political equality via fair mediation, and foster meaningful deliberation by, for example, surfacing trustworthy information. We also point to key challenges that remain. Ultimately, a range of empirical, technical, and theoretical advancements are needed to fully realize the promise of AI-mediated deliberation for enhancing citizen engagement and strengthening democratic deliberation.

Can AI mediation improve democratic deliberation?

ArXiv.org · 2026-01-09

The strength of democracy lies in the free and equal exchange of diverse viewpoints. Living up to this ideal at scale faces inherent tensions: broad participation, meaningful deliberation, and political equality often trade off with one another (Fishkin, 2011). We ask whether and how artificial intelligence (AI) could help navigate this "trilemma" by engaging with a recent example of a large language model (LLM)-based system designed to help people with diverse viewpoints find common ground (Tessler, Bakker, et al., 2024). Here, we explore the implications of the introduction of LLMs into deliberation augmentation tools, examining their potential to enhance participation through scalability, improve political equality via fair mediation, and foster meaningful deliberation by, for example, surfacing trustworthy information. We also point to key challenges that remain. Ultimately, a range of empirical, technical, and theoretical advancements are needed to fully realize the promise of AI-mediated deliberation for enhancing citizen engagement and strengthening democratic deliberation.

Functional reorganization of motor cortex connectivity during learning

bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-05 · 1 citations

Learning new tasks requires the brain to reshape the flow of neural activity, but how these changes arise from dynamic neural connectivity remains unclear. Here, we used two-photon photostimulation and calcium imaging to map learning-related changes in connectivity in layer 2/3 of mouse motor cortex, induced by learning of an optical brain-computer interface (BCI) task. Mice rapidly (within minutes) learned to change activity in a conditioned neuron to earn rewards. Activity changes were sparse; the conditioned neuron increased activity more than surrounding neurons. Mapping connectivity before and after learning revealed changes in motor cortex connectivity, enriched in neurons that were active before trial initiation, analogous to motor cortex populations that are active preceding movement. Motor cortex plasticity reroutes preparatory activity to neurons that are active later and control the conditioned neuron. Our findings show how rapid learning can be achieved through structured changes in motor cortex connectivity.

AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning

bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-21

Scientific models are widely used across the natural sciences as an interface between scientific theories and empirical data. Such models play a key role, for example, in the study of human and animal learning, where they express algorithmic hypotheses and relate them to psychology and neuroscience data. These models are traditionally handcrafted by expert researchers based on existing theory or new insights. Such handcrafted models, however, are now known to fall short of capturing the full richness of behavior, even in their narrow domains. An alternative data-driven approach has emerged, seeking to discover new insights by fitting and interpreting flexible models. However, these tools require substantial human effort to derive insight from data, and it has been unclear how to discover new ideas from data efficiently. Here, we present DataDIVER, a general approach for automatically discovering computational models from data, and demonstrate that these models surface novel mechanistic insights into human and animal learning. Our approach delivers models that take the form of short computer programs, which are optimized both to fit data well and to be simple. These programs explicitly connect with existing theoretical frameworks and are readily understandable by human scientists. They can also be used to make novel predictions, some of which we show are borne out in re-analysis of existing data. General-purpose tools for surfacing new ideas from data, especially in combination with the large datasets that are increasingly available in many fields, stand to dramatically accelerate scientific discovery.

Depression as a disorder of distributional coding

ArXiv.org · 2025-07-22

Major depressive disorder persistently stands as a major public health problem. While some progress has been made toward effective treatments, the neural mechanisms that give rise to the disorder remain poorly understood. In this Perspective, we put forward a new theory of the pathophysiology of depression. More precisely, we spotlight three previously separate bodies of research, showing how they can be fit together into a previously overlooked larger picture. The first piece of the puzzle is provided by pathophysiology research implicating dopamine in depression. The second piece, coming from computational psychiatry, links depression with a special form of reinforcement learning. The third and final piece involves recent work at the intersection of artificial intelligence and basic neuroscience research, indicating that the brain may represent value using a distributional code. Fitting these three pieces together yields a new model of depression's pathophysiology, which spans circuit, systems, computational and behavioral levels, opening up new directions for research.

Deep mechanism design: Learning social and economic policies for human benefit

Proceedings of the National Academy of Sciences · 2025-06-16 · 5 citations

Human society is coordinated by mechanisms that control how prices are agreed, taxes are set, and electoral votes are tallied. The design of robust and effective mechanisms for human benefit is a core problem in the social, economic, and political sciences. Here, we discuss the recent application of modern tools from AI research, including deep neural networks trained with reinforcement learning (RL), to create more desirable mechanisms for people. We review the application of machine learning to design effective auctions, learn optimal tax policies, and discover redistribution policies that win the popular vote among human users. We discuss the challenge of accurately modeling human preferences and the problem of aligning a mechanism to the wishes of a potentially diverse group. We highlight the importance of ensuring that research into "deep mechanism design" is conducted safely and ethically.

How should the advancement of large language models affect the practice of science?

Proceedings of the National Academy of Sciences · 2025-01-27 · 47 citations

Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advancement of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and overhyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices.

Whole-body physics simulation of fruit fly locomotion

Nature · 2025-04-23 · 24 citations

Abstract The body of an animal influences how its nervous system generates behaviour 1 . Accurately modelling the neural control of sensorimotor behaviour requires an anatomically detailed biomechanical representation of the body. Here we introduce a whole-body model of the fruit fly Drosophila melanogaster in a physics simulator 2 . Designed as a general-purpose framework, our model enables the simulation of diverse fly behaviours, including both terrestrial and aerial locomotion. We validate its versatility by replicating realistic walking and flight behaviours. To support these behaviours, we develop phenomenological models for fluid and adhesion forces. Using data-driven, end-to-end reinforcement learning 3,4 , we train neural network controllers capable of generating naturalistic locomotion 5–7 along complex trajectories in response to high-level steering commands. Furthermore, we show the use of visual sensors and hierarchical motor control 8 , training a high-level controller to reuse a pretrained low-level flight controller to perform visually guided flight tasks. Our model serves as an open-source platform for studying the neural control of sensorimotor behaviour in an embodied context.

Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem

Nature Communications · 2025-03-22 · 7 citations

A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design a social planner that promotes sustainable contributions from human participants. We first trained neural networks to behave like human players, creating a stimulated economy that allows us to study the dynamics of receipt and reciprocation. We use RL to train a mechanism to maximise aggregate return to players. The RL mechanism discovers a redistributive policy that leads to a large but also more equal surplus. The mechanism outperforms baseline mechanisms by conditioning its generosity on available resources and temporarily sanctioning defectors. Examining the RL policy allows us to develop a similar but explainable mechanism that is more popular among players.

Author Correction: A virtual rodent predicts the structure of neural activity across behaviours

Nature · 2025-08-11 · 2 citations

Following publication of this article, we discovered a bug in the code that extracted and sorted spikes from the neural recordings.While the sorting itself was good, the final step in the analysis pipeline inadvertently introduced short periodic dropout of spikes for a fraction of the cells, causing the cell quality filters we used to exclude units that were otherwise well-tracked.We have fixed the code and reanalyzed the recordings, which now include N = 2,654 and N = 1,177 putative single units for the dorsal lateral striatum and motor cortex, respectively (previously, these numbers were 1,249 and 843; edits to values made in the sixth paragraph of main text).In the Fig. 3c legend, the number of neurons predicted now lists 1,788 neurons in DLS and 1,095 neurons in MC, versus 732 and 769, respectively, in the original.None of this had any impact on the findings or conclusions of our paper.Indeed, all our results remain unchanged, with effect sizes comparable to those in the original publication.Figures 3,4 and Extended Data Figs. 1, 4-10 have been updated.For explanation of changes and comparison to