Hybrid human-AI networks in 5x5 grids reached lower final polarization than human-only networks after eight rounds of opinion revision on polarizing topics.
How will advanced ai systems impact democracy?
4 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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Using survey and experimental data, the paper reports that conversational AI is a common source of political information in the UK and that its effect on belief in true versus false statements statistically does not exceed internet search, although the equivalence claim is fragile.
A GNN-RL social planner in networked CPR games with overlapping pools achieves higher cooperation and lower inequality than baselines across four network topologies, distilled into resource-dependent and degree-conditioned mixture rules.
A game-theoretic framework and algorithms are introduced to maximize beneficial information from ML systems while minimizing biased influences arising from conflicts of interest.
citing papers explorer
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An Experimental Method to Study Opinion Diffusion in Human-AI Hybrid Societies
Hybrid human-AI networks in 5x5 grids reached lower final polarization than human-only networks after eight rounds of opinion revision on polarizing topics.
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Conversational AI increases political knowledge as effectively as self-directed internet search
Using survey and experimental data, the paper reports that conversational AI is a common source of political information in the UK and that its effect on belief in true versus false statements statistically does not exceed internet search, although the equivalence claim is fragile.
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Exploring cooperation mechanisms via reinforcement learning in network common-pool resource games
A GNN-RL social planner in networked CPR games with overlapping pools achieves higher cooperation and lower inequality than baselines across four network topologies, distilled into resource-dependent and degree-conditioned mixture rules.
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Learning with Conflicts of Interest
A game-theoretic framework and algorithms are introduced to maximize beneficial information from ML systems while minimizing biased influences arising from conflicts of interest.